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<meta name="author" content="Authors: Brian Boyle, Yen-Chieh Liao, Sarah King, Robin Rauner, and Stefan Müller" />

<meta name="date" content="2025-02-08" />

<title>Catalysts for progress? Mapping policy insights from energy research (ERSS)</title>

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) 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<h1 class="title toc-ignore">Catalysts for progress? Mapping policy
insights from energy research (ERSS)</h1>
<h3 class="subtitle">05_stm_topic_model.R</h3>
<h4 class="author">Authors: Brian Boyle, Yen-Chieh Liao, Sarah King,
Robin Rauner, and Stefan Müller</h4>
<h4 class="date">2025-02-08</h4>

</div>


<pre class="r"><code># load required R packages
library(tidyverse) # CRAN v2.0.0
library(here) # CRAN v1.0.1
library(quanteda) # CRAN v4.1.0
library(quanteda.textstats) # CRAN v0.97.2
library(stm) # CRAN v1.3.7
library(furrr) # CRAN v0.3.1
library(patchwork) # CRAN v1.2.0
plan(multisession)
library(xtable) # CRAN v1.8-4
library(Hmisc) # CRAN v5.1.3

# load custom ggplot2 scheme
source(&quot;function_theme_base.R&quot;)

# If the code does not run, one or more packages may have been
# updated, which may result in errors or conflicts. You can solve this issue
# by installing the package version listed above or by using the
# groundhog package:
# after installing groundhog using install.packages(&quot;groundhog&quot;)
# change library(name_of_package) to
# groundhog::groundhog.library(name_of_package, date = &quot;2024-01-31&quot;)
# Instead of adjusting the library() function for each package,
# you can adjust them at all once using the
# the following syntax:
# groundhog.library(&quot;library(&#39;pkgA&#39;)
#                   library(&#39;pkgB&#39;)
#                   library(&#39;pkgC&#39;)&quot;, date = &quot;2024-01-31&quot;)
# More details are available at: https://groundhogr.com/using/

# Note: This script requires data containing the text of abstracts.
# Due to Scopus&#39; data-sharing policies, these files cannot be shared publicly.
# Please contact us if you would like to reproduce this script.

# Model diagnostics
# Workflow
# Abstracts collected for top 100 ranked energy journals by SJR rankings
# Filtered to remove non-journal articles, and those containing valid doi and author info
# Second filter applied to identify climate and energy publications, based on keywords

# Gives us 270,537 article abstracts

# Import data
# Dictionary classification used to identify policy statements within abstracts
abs_coded &lt;- readRDS(&quot;data_dontshare/data_scopus_1_100_2010_2023_classified_dontshare.rds&quot;)

# 40,468 abstracts contain a policy statement
# 15% of corpus
table(abs_coded$policy_statement)</code></pre>
<pre><code>## 
##      0      1 
## 230069  40468</code></pre>
<pre class="r"><code>mean(abs_coded$policy_statement)</code></pre>
<pre><code>## [1] 0.149584</code></pre>
<pre class="r"><code>theme_set(theme_baser())

# Filter only abstracts flagged as containing policy statements
abs_ps &lt;- abs_coded |&gt;
    filter(policy_statement == 1) |&gt;
    select(journal_name, rank, article_cover_date, article_title,
        abstract_text = text, article_keywords,
        source_id, publication_type_sub, journal_issn,
        article_scopus_id, article_year, article_doi
    )

# Remove original object with all abstracts, as only modelling policy statement ones
rm(abs_coded)

## Approaches for exploring sub-topics of policy statements
# Use the author attached keywords for each article
# Use the abstract text in topic models

# 1. Create corpus and DFM


# Text preprocessing

# Create a dictionary of common and uninformative words in academic abstracts

stopwords_academic &lt;-
    c(
        &quot;model&quot;, &quot;models&quot;, &quot;using&quot;, &quot;find&quot;, &quot;findings&quot;, &quot;can&quot;, &quot;data&quot;, &quot;paper&quot;,
        &quot;results&quot;, &quot;study&quot;, &quot;used&quot;, &quot;use&quot;, &quot;may&quot;, &quot;approach&quot;, &quot;analysis&quot;, &quot;also&quot;, &quot;based&quot;,
        &quot;found&quot;, &quot;article&quot;, &quot;method&quot;, &quot;however&quot;, &quot;due&quot;, &quot;elsevier&quot;, &quot;ltd&quot;, &quot;rights&quot;,
        &quot;reserved&quot;, &quot;effect&quot;, &quot;causality&quot;, &quot;all&quot;, &quot;total&quot;, &quot;methods&quot;, &quot;many&quot;,
        &quot;proposed&quot;, &quot;different&quot;, &quot;within&quot;, &quot;two&quot;, &quot;show&quot;, &quot;this paper&quot;
    )

# Create corpus

corp_abs &lt;- abs_ps |&gt;
    corpus(text_field = &quot;abstract_text&quot;)

# Run pre-processing steps and convert to tokens object

tokens_abs &lt;- tokens(corp_abs,
    remove_numbers = TRUE,
    remove_punct = TRUE,
    remove_symbols = TRUE,
    remove_separators = TRUE,
    remove_url = TRUE
) |&gt;
    tokens_tolower() |&gt;
    tokens_remove(c(stopwords(&quot;english&quot;), stopwords_academic)) |&gt;
    tokens_select(min_nchar = 3)

# Check for collocations, i.e. multi-word tokens, based on corpus text
set.seed(235)
tokens_colloc &lt;- tokens_abs |&gt;
    tokens_sample(size = 5000) |&gt;
    textstat_collocations(size = 2, min_count = 5, tolower = TRUE)

# select top 200 multi-word expressions to collocate
top_cols &lt;- tokens_colloc[1:200, ]

# Add collocations into tokens dataset
toks_abs_cols &lt;- tokens_compound(tokens_abs, pattern = top_cols)

# Create document feature matrix from tokens object
dfm_abs &lt;- dfm(toks_abs_cols) |&gt;
    dfm_trim(min_termfreq = 5, min_docfreq = 1)

dfm_3y &lt;- dfm_subset(dfm_abs, article_year %in% c(2010, 2015, 2020))

# Prepare dfm for topic model, set metadata

# Filter DFM to keep on 3 years

# We can run topic models with different numbers of topics
# and compare model evaluation metrics
# Set &#39;k_test&#39; object to TRUE to run
# Note: warning message &quot;Dropped 27,306 zero-count feature(s)&quot;
# will appear because the subsetted dfm does not contain many of the
# features from the full (=all years) dfm

n_topics &lt;- c(5, 10, 15, 20)

stm_ntest &lt;- tibble(K = n_topics) |&gt;
    mutate(topic_model = future_map(K, ~ stm(dfm_3y, K = ., verbose = FALSE, seed = 135),
        .options = furrr_options(seed = TRUE)
    ))</code></pre>
<pre><code>## Loading required package: quanteda</code></pre>
<pre><code>## Package version: 4.2.0
## Unicode version: 14.0
## ICU version: 71.1</code></pre>
<pre><code>## Parallel computing: 16 of 16 threads used.</code></pre>
<pre><code>## See https://quanteda.io for tutorials and examples.</code></pre>
<pre><code>## Loading required package: quanteda</code></pre>
<pre><code>## Package version: 4.2.0
## Unicode version: 14.0
## ICU version: 71.1</code></pre>
<pre><code>## Parallel computing: 16 of 16 threads used.</code></pre>
<pre><code>## See https://quanteda.io for tutorials and examples.</code></pre>
<pre><code>## Loading required package: quanteda</code></pre>
<pre><code>## Package version: 4.2.0
## Unicode version: 14.0
## ICU version: 71.1</code></pre>
<pre><code>## Parallel computing: 16 of 16 threads used.</code></pre>
<pre><code>## See https://quanteda.io for tutorials and examples.</code></pre>
<pre><code>## Loading required package: quanteda</code></pre>
<pre><code>## Package version: 4.2.0
## Unicode version: 14.0
## ICU version: 71.1</code></pre>
<pre><code>## Parallel computing: 16 of 16 threads used.</code></pre>
<pre><code>## See https://quanteda.io for tutorials and examples.</code></pre>
<pre><code>## Warning: There were 4 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `topic_model = future_map(...)`.
## Caused by warning in `dfm2stm()`:
## ! Dropped 27,306 zero-count feature(s)
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining
##   warnings.</code></pre>
<pre class="r"><code># Extract evaluation metrics from each model
heldout &lt;- make.heldout(dfm_3y, seed = 235)</code></pre>
<pre><code>## Warning in dfm2stm(x, docvars, omit_empty = TRUE): Dropped 27,306 zero-count
## feature(s)</code></pre>
<pre class="r"><code>set.seed(25)
stm_eval &lt;- stm_ntest |&gt;
    mutate(
        exclusivity = map(topic_model, exclusivity),
        semantic_coherence = map(topic_model, semanticCoherence, dfm_3y),
        eval_heldout = map(topic_model, eval.heldout, heldout$missing),
        residual = map(topic_model, checkResiduals, dfm_3y),
        bound = map_dbl(topic_model, function(x) max(x$convergence$bound)),
        lfact = map_dbl(topic_model, function(x) lfactorial(x$settings$dim$K)),
        lbound = bound + lfact,
        iterations = map_dbl(topic_model, function(x) length(x$convergence$bound))
    )</code></pre>
<pre><code>## Warning: There were 8 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `semantic_coherence = map(topic_model,
##   semanticCoherence, dfm_3y)`.
## Caused by warning in `dfm2stm()`:
## ! Dropped 27,306 zero-count feature(s)
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 7 remaining
##   warnings.</code></pre>
<pre class="r"><code># 03 Model evaluation and inspection

# Figure A6
# Plot model evaluation statistics across range of topics

ntopics_eval_plot &lt;- stm_eval |&gt;
    transmute(K,
        `Lower bound` = lbound,
        Residuals = map_dbl(residual, &quot;dispersion&quot;),
        `Semantic coherence` = map_dbl(semantic_coherence, mean),
        `Held-out likelihood` = map_dbl(eval_heldout, &quot;expected.heldout&quot;)
    ) |&gt;
    gather(Metric, Value, -K) |&gt;
    ggplot(aes(K, Value)) +
    geom_vline(xintercept = 10, linetype = &quot;dashed&quot;) +
    geom_line(linewidth = 1.5, alpha = 0.7, show.legend = FALSE) +
    facet_wrap(~Metric, scales = &quot;free_y&quot;) +
    labs(
        x = &quot;K (Number of Topics)&quot;,
        y = NULL,
        title = &quot;(a) Model Diagnostics by Number of Topics&quot;
    )

# Compare exclusivity vs coherence for specific numbers of topics
ibm_five &lt;- c(&quot;#648FFF&quot;, &quot;#785EF0&quot;, &quot;#DC267F&quot;, &quot;#FE6100&quot;, &quot;#FFB000&quot;)

ntopics_excl_sem_plot &lt;- stm_eval |&gt;
    select(K, exclusivity, semantic_coherence) |&gt;
    filter(K %in% n_topics) |&gt;
    unnest(cols = c(exclusivity, semantic_coherence)) |&gt;
    mutate(K = as.factor(K)) |&gt;
    ggplot(aes(semantic_coherence, exclusivity, shape = K, color = K)) +
    scale_colour_manual(values = ibm_five) +
    geom_point(size = 3) +
    labs(
        x = &quot;Semantic Coherence&quot;,
        y = &quot;Exclusivity&quot;,
        colour = &quot;Number of Topics&quot;,
        shape = &quot;Number of Topics&quot;,
        title = &quot;Comparing Exclusivity and Semantic Coherence&quot;
    )

topic_eval_plot &lt;- ntopics_eval_plot / ntopics_excl_sem_plot

topic_eval_plot</code></pre>
<p><img role="img" 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Iceekgtvvjizqpcvx9nwioj8/hduarGWMBvFcv56xHw20F92OYZivrNwATHpEmTtN1O2K0r4veYJwdXWWgz7iXQZs7jN19mJt9//72CzUy0sRXDoYceqqKER83cX8wTsMQff7gnJQWMaWjfv/XWW4WYICliHBX9XMRvG7/zNA4ck/jGnc/jXht1H5plllniqm6ac3kwStNZjCk8l/C7sRUG0uTNM01besbkyY1lkUBRBKgBWhRZlksCJNAyBPAiEbTJhCW6ccuBTOchyLj66qvVtddeq188XQLPhRdeWG2++eaqT58+apNNNjFZY7e9evWqaBOWAGJ51HHHHRebr9qTcE4S1OqEAHTPPfcMFQkHScHJ5oYbbqgFZKHEKSOgcQqOsBMI4ad5QZhppplU586d1UorraQgpMVy6xlmyP5ow/W95JJLtOD23Xff1e2fY445tL1VLAGHnVXUVW3ABPyiiy5ScA4EbUjzErvIIosoaFZAOxJLODEWmjVAwxfL2XfYYYdQF3DdMD5nn332inNfffWVU2ML2qQbb7xxRVr7oKjfFep45ZVX1IUXXqheffVVhbFgxhp+73vssYc67LDDVLt27fS1hMDFDgsuuKDWQrPjPvvss9DvBNol0CpHgHD1ggsu0BrcSGtCx44dFTz87r///trcholPuy3qN4PfyhlnnKHGjx+vXy5x7zEBy9aNxjB+i9tss42accYZzemK7QMPPKCMYBzjIBjgBAS/PROGDh2q75M4rnbcIC/uTVdeeaV66qmntLAZv00juMW9A79J3K/wm19++eWRJTHkxSSxoioSYDxffPHF+nmBMQHhJ8bvqquuqjbYYAN1xBFHKPx2gwGrCswYtc9hrOJ+GxdQD1YpBANsZsPZVVEB4+If//iH/v3WWscbb7yhDjnkkFAx559/vurSpUso3kTsuuuu6ssvvzSHeovnO8ZvMFxxxRXqn//8Z0U08sO5IgI0WnHfHD16dIXQunv37lpzHvMAO+CeddZZZ6m7775b/fHHH/6pxRZbTJeJ/sw999x+fJadasdRXB15PRdhguW0006rqApzNLDAfAHscT8xH5bwQXbQoEH6mQtb7rWGPO61V111lf/ssJ8Ddtvs+yHi8VyN0ti085VhPw9Gph8wAXXkkUeaQ71dYokl9BwRB3Ckh7kIFAPgkBNhzjnn1I4ZMZfFeEgSJvMZo7HxHwm0JgGZCDGQAAmQAAnEEBDBGNRlKv5OOumkmBz/PSVCT2+22WaryBcsJ3i80047eWJbMbFs0WQIlSvCksR8SQlEYyRULtooQk1PhAMV50QT1RNhQqhIEdhUpEN+eZHz5CUsFH/yySeH8tsRP//8s9e3b99QviA3cywv9Z5Mfu0iYvdFuOWdeuqpob6Z8sxWNG490RDz5KU+1BYRmMTWIcKWVONAPLp68uIaWxZOikA+1Aa0U0wgJOaNSyCaEs5yxZFRXLbQufXXX99ZjgjQQ2mr6UtRvyuM5XPOOccTgZ2z/WYsYBw///zzHvpj4sxWXsJCfXT9pkRIqNOJ/VSvffv2oXJMedjKC7p3zDHHeCKkC5XtiijqN4NrJYKc2Lba7ca+CI09EZS6munhdxFMH3csH0D8cqoZN8gsL+GeaCunqleEuZ4I82Pvx3kz8TuYYUccoTj78+mnn3oiYPNk2avzvGG9wgoreKJ1HKoR93yTxt6KwDSUNhiB56Odx+yDfzVBhHnO8ky59ha/FxHWOavZbrvtQuWIKRtn2qeffjqUFvWImQ9nehMpdsFD+eRDoTldsRVBTCgtfuuTJ0/2Nt1009A5u5/YF/Mhujzcu+Tjpzf99NPH5lluueU8MeVT0QZzUNQ4MuUHt3k+F11tlw9o+pmNe1CQG47l40awSZmP87zXikDP2U5X202crCLK3OakDLvssouzHR999FFSVuf5PBmZCh588MFQG3EfQ5APa5580AmdN8ywlY/OnqzCMcU5t3zGOLEwkgRaggC+KDKQAAmQAAnEEBAti9BkCi+HcUG+zCe+jNgTMns/rcAJAjM7H178vvvuu7hmJZ5zCWtQB14oIQS068O+aMmEylxmmWVC6UQjJrMAFIIT0dQIlRVsQ/BYNDU9CFYh3IwLECiJxm3q8mUJvyeOpkLpowSgou2rBSjB9iUdiyaoN3Xq1MimVzsxjyzw/0/kJQAFe1cfXcLurH0p6neFsSLais52u/oiGpyeaC+G0mcRgIpt1Ez3CAhLkkJRvxmMZbw0ulgkxYmGkvOjRL0FoCNGjEj1ISLYHwiiXL/HIpgkXV/XeZfwB33YaqutUl8vCAzuvPPOiuIhVBMt5FAZEPAlBdGQDOVbZ511krJFno8SgIpNxFA96LvYOXTe/5tBACpasp5o2Dr7FRybOMYHWrHDnDo9PpTKEuQQ66LGUbCiIp6LrraLFrcn2n6RXPDhs5aQ9722FQWgeTMy1ytKAHrLLbckfsA0vyE8w0Ur2BQZ2madm6CAVn3GhOAwggSanAAFoE1+Adl8EiCBYgnI8k5Pls6EJtFivzGyYmhvyPKaUB4z8UqzhYZnUnBN7sVTZ1K22PNxAlDXBB0ac3aAlmSwf8suu6xOkkUDVOx0eWJPMlRWsOy4Y1nCaTcttO/S7I0rL+qcLFsPlY2IE088ser2H3XUUc4yEVnNxDyyMOtEXgJQaGC4WLkE+1n6UuTvCi8urjbHxbm0rdIKQKFNDUF9XPnBc7gP4XcRFYr8zchydmdbZTmtB2GXmJ/wxLyBMw36gQ8HwVBPAaiYMkj9YhzkjmOXBl8RTIKM0hy7hD+uPiTFLbTQQiFt16h72MsvvxzZNFnW7BwHuN9WG6IEoLJsWGsZu/omy/5D1TWDABSax67+RMVlTY9ybG1qA6nIcWTqwDZqTEX1z46Pei662u66P5uywAzPnmpDEfda1/zKtDdqW2YN0CIYmevlEoDiIw6UAKJYueLFiacHgbwrZJmbIH8rP2NcfBhHAs1MoHbDJ3JHYSABEiCBViUgS13VL7/8UtE9eVFUon1ZEWcfXHbZZRX2t3BOJtxqyJAhCnYzYf9RNDW152zYBnMFOJRJCi47bE888URStqrPw3ZSMAQdIQXthCI97OllDbAV57KDBY69e/dWsE93xx13qGOPPVbJ0idn8bAjKctAnedwTU844QTnubXXXlvbKzWOHq655prY6+0qBA5VYCcxGDBurrvuOu1wBeXfd999SjRmg8nUmWeeqe1YhU40QQTsYLoC+ltLKOp3hd8j7Aa6AsY8xri8zClZAqhtjBmba8Y2qJ1PJoT2YeQ+nEAY25mw6wY7f3CKJhozSgQE+n4RzIwxK2YOgtH+cVG/GfQJtuaCAbYQYb8U9taee+45zWjMmDHarmQwLWy2yZLiimjY4dt99931X9A2LBKKpqF/HumWWmqpivxZDg466CDfzqedD7/1G2+8UdsCRRtFS1nbirPTYP/666/XTmVMfFFMTPl5bsWsirYhifEjH7i03VV4gw4GOLPCfccOsHmLe24wuMaDSSOazWbX36K+Imx/wq4fngWuIEvJC3UM6KozjzhzD8F9dNiwYQq2SOHgEGPQdd1MetQt2sr6t4r7FWxNw1aoK6SZXwTz1TKOTFn1fC4G78+w2wp74RjPsBe+hNiMrDYUca8VDWltlxS2SXFfcgXR9PXTIJ3rvunK14i4IhjF9QO2PkVrXSeB7WY8U2ETF2MdtrpFIB7KjvF43nnnheKriWjLz5hqeDEPCTSUgDw4GUiABEiABCIIyAth6IsylkTGBdgUEwc6FflEGOfMAnufwaXs8lDwZDLlTG9HygSvog7kwzLVWkKcBqi8QIdsZULrC1qyJogDh1CbxMmDPp1WA1SEKaEy0Le//vWv3r333muq8rfQDpLJtjMPlri7gghOnemhpeha7gptgChbYi4NUHnZD5WPJVcikA01B2PAtdS/f//+obSIyKqZ4CzEEZmXBihsa+F6Bf9cNmqz9KWo35UIwkNtRdthC81ld1ME886lwcgDreVgiPpNIf1GG23kXKoLTacgPxzDBq0rFPmbcdkbxj0rysQE+GCsQxsH41o+WHinnHKKJwIZV9N1nAgjQv0VoWdk+izjBlrxLpa77bab8/rCtqtLe0wcyPjtqQcTv7KEHZf2m+kv7nOuIMJep5082KyGuRI7YIya8swW1ysqwA6zSWe222+/fVTyVPFRGqBY8oqw9dZbh+pE3fgN26EZNEDRbow/2CANBnH04+wn8uD+Gnx2yccdp2Y2bIEGQ9HjCPUV9VyMa/tmm23m2fYrsSzbZY86yCPquMh7rakT90vz27G35nyR2zxsgBbNyKUBajhtu+22IU128MJyd5dtUKyskA8MIaR8xoSQMIIEWoYAl8C3zKVkR0iABIogIF+OQxNROPlJCrCfBmcDePmGgEW03yKzwFaVmbyZbdzLvynI5YQFgolaQpSwxjiVgB0300aztV/UsIzSxJuteNPUTUorABUPuKEyUBZecuLCeuut58wnHmJD2Vw26mC2AELAqCCewZ3lBwWgoknlTHf66adHFe3BGY7hZbYQIuHFPxiyTMyDeeOO8xKARvXfZTswa1+K+F25bFviRQn9iAqijRW6XrhuWQSgcH5kfhvBeiC4cC3ni3LYUuRv5sUXX3T2Vbyke1GmQCDoT+PMzfS7SAEoBCDmN2W2uL6ul17THuOABqYK4CylT58+Hn7/JtSDiakraRsl/AFT++NUsByXszrwCTpTw7HhZm/BIBjwzLPTmH3xJh9Mmuk4SQCK8RblcFBWRfh1NYsAFMuhXQHCO8PU3sKcRpTZnC233DKUB8/pYCh6HEU9F/J4Lka1Hb/zWu2iBzkVea81dTW7ALRoRlECUHyQj3qmgi2chtm/G7MPUxrBkGVu0urPmCAbHpNAsxPgEni5+zGQAAmQQBQBsTsYOiV270JxwQgssxIHDUoEFur4449Xor0YTKKXwD788MNKJmyhcz/99FMoLhjhageWGWN5bVEhbhm8vIApecmpqBpLzsRbfEVc3IG86CrR6gklWWWVVZRoJoTi7YizzjrLPvT3sQwqGD7++ONglOrXr58Sh0aheBMhk3olQklzGLkVG3jOc/Ii6oxHpGiqKhF4VZwX76l6SXRFZBMciBaSs5XB/jkTJUQW8btyjQUReKmopfxoYtJYSeiGPr3FFltE/jZEmKNEmBoqBsv8gqHo34xo9Cn5OBCsVuH3Bka4J4hDEb3M2lx7tL0syzNdv0eY0RABdKhPJuLcc89Vr732mhIhrl42fvPNNyssPzWhGZhgieyMM85omhzaijAgFIcImHqwg2hvOu97rmXwMEsSDDAZEXfvC6av5hjjDc9ZVxBBr29uwnW+jHGixeZslqxCcMZjebdodTrPwWRPMODZkjbkNY5cv0O0IW5s1PpcFIG3mmeeedJ2NTFd0ffaxAY0QYJGMhK7zJHPVKCTj3ZKPmqFKMqHm1BclgjX2G4Lz5gsjJiWBMpEoPJtq0wtY1tIgARIoAQEYHsrGFyCx2Aa1zHsfsLmI+xWwlaXeBVXsAEKwWEwBO1XBc/j2CWMk69ySrSyXMlziYsTgAbtgaLCrPY/8fINm4zBIEscnbbo7HSwm2XsM9rxsKtoB9gthGAjGGA3Ki5AKAWhdlJ4//33nUkgmMY519+ECROU60UV9hWbLbjYog+ujwB59K2W3xXGAmwjBkOUMMGkgz0xCPdrCUnjzXWfkSX5oSqL/s2IdlmkTTqMadj2hA3VDTbYQF/jbbbZxrdpGmpsnSMgkMVvKxiSfsewKwwhp8vmIspqBiZJ4xM2VUVDLohGBT8IIA0+CATDbbfdhlVkFdGICwbkjRPEBtNXewwb2yuuuGIouzgn0cL60IkSR0TdG1wfItANlz1w0z3XPCHN/MLkz2scNeK5CFufeYai77V5trVRZTWSkZiBiO025liujwhoc7WhLT9jqmXGfCTQaALhzyCNbhHrJwESIIESEXBpgLpeKKKa/Oyzz6qbbrpJPfXUUwovYsEXxqh8LoPtwbRR7YBQJ057LVhOluN1111Xv8zaghjxCKygUeJygOQSmMbVJ0sZnaeTBBYmE9IFBcBBAWjUZNelcWfKNVtoiL7++uvm0LkVG5jO+KTJuStTVFmutGWJi9JezlMAmtfvKmosiLmGRJxp0sQVksQjStgRLLMev5krrrhCiYkJ5bof2u2Bhio+8uAPAR964FhHzAzYyeq2D80e4xjDrtT1EmyfT7NfdiZp7mdiviOk8en6Tey5554K/bUDHIjIMngF5y0IGIdiKsVOovfhwKoeAZpdcJS2/vrrh56z0FCG1nYeIekZ7hpvWeuFcydXiBLIt2vXzpVcx7mE3JGJHSfyGkdRz7Iin4u13qODOOpxrw3W2WzHjWQkZj8ScWFMBNtYiwZoW37GJMJmAhIoKQFqgJb0wrBZJEAC5SAA7bJgiHo5sdP98MMPWiMKL2OXXnqp1vJMenGy86dZLhzVDpdGm112LftY1rrmmmtWFAEtMAg/IeS1A5YrZxWAunijzA4dOthFR+670gVfvOBt2xWiBMp22jSC5Vom03Zd2HdpwwbTlO3YpdGMNrquTda25/27ihoLaZZvJwkwk/qWJJiIEnYEy63HbwYe2+FNN+tyUnE8oSDggBfeRgSx4+asNure6UwcEVl2JuKIL6Ll/4t2aZ27Vj1Au96lFW2bK3Etf0ee4PPif7Xnv4cPdHvvvXeoYAjmsZQ7yzM4VMj/RySVkUW70lUHPnxEPf/xTHWFuGsdlcdVjisurmyTPs04asRz0bUixLS5mm097rXVtKtMeRrJKO5DgGHkMskk9s/N6czbtvyMyQyLGUigJAQoAC3JhWAzSIAEyknANflPEjBCMwbaUqNHj3Z2Ci84EIyKowMFAYHLBlbUC5BdoLG3Z8dhv1bBTLC84LFLqHnGGWeooOYflvFhmX+W4JqcIn9QqzOqTJeGWlCDxfWylraOH3/8MapqPz6N8MxPnLDz22+/JaQo3+koDVkIJ2oJRfyuosZbmhci19LqLP1LEoCmFVxE9SHP3wz6hSXh0A486aSTFJZPpw3QiINNzWuvvTZtltzSRdn5dNldrqbSMjNx3QuDfXTdz6K0Y6EFGgy33367L1TEfjDstttuwajCj/Escj137r///shncpZGJQk4YQOxllCNuQCYZCgq5DWOGvFcdM3fauFUr3ttLW1sdN5GMor6oGkzgZ38YIAmfLWhLT9jqmXGfCTQaAJcAt/oK8D6SYAESk0Ak5ugRptLQ8Z0AtohWPaJ5e52wJL2PfbYQ+266656yaC9vFW8C9tJ9X6aJfCul1dkzlvrIdg42PUcNmxYRfSYMWMqjnGQ1f4n8kRNRF3LMpE+GIL263A+aB8tSniTRmCURugl3s6DzdJOZGArMWso+lpmbU9Segi77r33XmcyfBSoNhT1u4paMpdGABpcRpe1b2k+cqQpsx6/GdMOaNjAhjH+4CRIvPEqOHLD7188jptkzi20QP/+9787zxUVucwyyziLDjprcyZKGVlWJnHPKdM11zh33b+QHsLMo48+WtkCQAgcnnvuOe2wK/gMgAAfz7t6B2gpw0GXa6y5hB9JGp3B9kd9eDTpkj6QmnTNss1rHLnGFeZBRT4X7XlWHrzrea/No72NKKORjPCRNCm4hKRRc8KksnC+LT9j0vBhGhIoIwEKQMt4VdgmEiCB0hBwaZLEaQ89+uijIeEnOnPJJZeo/v37O/vl8uycRjjiEoDipTPrMlVno2Ii4VgAWiq2HVBXcpemqCudHYdl0uhD8KUUtuaSAoSTrsltUAAKoTaWwAZfVGHLNC6gv2mW8XXs2DFUDLSCYD80zst8KFMTRtx5551OpzPQlMOS4WpDUb8rvKxBeyoovINQD3YDowKWvZXFQVU9fjMuDljajr9jjjlGYXy/9NJL6plnnlHQtHP9XiF8gtB4iSWWcBUXisvDliKEk7iHwy6yHYIftexz2IdgFwJbONWBQyRs8YcX5biPU0UzCbYz7hiCpa222ioyCZaqup4hUfaWoTkPp324vnaAN3jkCd6zcf/P2wajXW/cPj42XnPNNVUJ10y5Udc5yskb8oFnrRqgpv6ybPMaR414LkZdw2rZNupeW217G5GvkYxggxie4KMCPl64hKRR97yocuz4tvyMsTlwnwSaiQCXwDfT1WJbSYAE6k7AtbwlTiMCWlHBAFt+ruWDJp1Lkyz4MmnS2tvgknOcm3feeWNf0O381e7DG/paa60Vmx1CzO7du8emcZ3EZNKVD1qFL7zwgiuLH3fccceFXsJxskuXLn4as+Oa8N51110qbsk5lvC6NIhMmWYbpU0QtJFq0mMLweqJJ56osIwUwhnYVW228N5776mBAwc6mz106FBnfNrIon5X+NAA4WwwvPrqq1oIFow3xxhrQaGpOVfvbdG/GQj+cW3vuecerVm3zz77qJNPPrmim9C0ghd4eIPH7xRbV3BpaCOdS1CR129g2WWXDTUF95PPPvssFG8icC/A7xBezU844QS14447qlVWWcX/aFIPJqYt1W6vv/762I9UF198sbNol6aeSeh6juGeZdsCNWnr5fzI1BfcwvZ2LUvDo0zJxGkPwzmbK6R5nrvylSEur3HUiOei675SC9Oi77W1tK0seRvJCGM17veJ8y676q75YBaebfUZk4UR05JAmQhQAFqmq8G2kAAJlI6AS2shTgAKb+jBAEFJlNbIlVde6dQqTFpmhzq+/PLLYFVOYU4oUQ4RScvboS1V7fLtqBfnAQMGKNeSTXQHL+A33HBDqGcQfrpsrLo0o3CN9tprr4olnqZAaP3GaQSadNhCQ9blMATLR6MErOedd54WtPTu3VvBdirspUFzNcrAvl1fo/fff/997XgE1zyoaYe24TeEftUSivxdHXLIIc6mwZlKUOgOodxhhx2mRo4c6czTqMiifjMQ3GAcYjz36tVLQZA9YsQIPVajbL2CAcaCK0Cb0hVcdg+j7pmu/HFx22yzTeg0lnHDBrNLyPrWW28pvCgHw3bbbafwcl8vJsH6sx7jOYVl4PaSdVMG7qMXXHCBOfS3uO/Eae6DZfC+DoFD8HeCj2Q77LCDX24jdjp37qwOP/zwqquOWklx0003OZniGbHffvtVXV9ZM+Y1jhrxXMSH2LxDUffavNvZyPIaxQja10OGDHHe1/HxGvaBgwEfSTbZZJNgdKbjtvqMyQSJiUmgTARkIsdAAiRAAiQQQUCWcnpyz674Ew2ZiNSed+ONN1akNXlFa8qTJZ0V+WS5qCcvis70m222WUVa18ERRxwRyiuaV66kqePk5T9UJvogS4sqynjsscec6Ux/DzrooIr0OJDlkKE8okkWSifCLk+WiofSomzRcPVEs8d78803PfEI7j355JOeLHlyphXtD0+WtYfKRwTqEO1eZz7R9vJw3UXLyxPzBJ4sh41sD9oky6hDdVx33XXOstdZZx1P7Jn66UUg6l122WWevKiF0ouQ2U9n74hQNJQW7RChnJ0s874IRZzlzjXXXJ4ItCr+RCDmyVJmT5xMOPOYcTDDDDN4GOdRIW1fivxdiRBM98W02d7Ky5G39dZbe6IFqMeZ2PuK7a843Ap1Neo3NWrUqFBaO0IEUaG6NtpoIzuJv1/kb0ZsFIfaAUYYy7KM2m+D2ZEXTU+0JUN54u6ba6yxRii9OIny7r77bv1bx2/k+eefN1V4accNMsjHJE/ML4TKRx9Ea1X/vsXRiyfmMzwRfEb+1h966CG//now8StL2JEPP86+mXG8/fbba3bmfiZL2D1xVOLMI5rNCbV5Hu7tpuyo7S677JJYTpYEIthw1ikfvmKLwf0V4y6qnYgXwbazDDyvo54RImTxZLm7zicfOD0x0eF17do1sh4RRDvrkA8KoTxinsWZ1kS6+uJ6jpr0YqIiVIdobJvT/rYe46io52JU22V1jd+/vHaKvNeaNp5yyimha4brXo+A365rjNnzlqR2FM0IczJXG00cnp1ikkXP4XAPwO9TPsQ688gHUGd3+IxxYmEkCbQEgfrcTVsCFTtBAiTQFgngpVG0YiomTrKkXb9Uu3jIUtGKtGZChu3aa6/tiRahJ16UPQg47XPBfQickoLYYwuVIUs7k7LFno8S1gQFoJhUQjgUbLc5dgl30gpA0cDRo0d7EJ6Z8qrZivZPbF/FLmts+RDuiWZabBq0yyUAxbiJmnCjTFl27Ylmamhs2f2U5ZTO9meZmDsLiIiMEoDabcq6L1pmEbX9NzptX4r+XYl5g8TrHOw7BMPBuEYJQEGzqN+MaGJGCszwGxHtXk+0Yj28SELYFrxfGkbiCT5yLIi2YIilyWe2tpAn7bgxFT7wwAOJ5Zt6XFu8UIsmpSnOqwcTv7KEnSjhT7AfuFZx92wIRcUuckJtnieav4ksxYZuYjlZElQrAEUdScKSKAEo8ooTp8i+4vm05pprenPPPXdFGtfHrFYQgJrxVMs4Kuq5GPUbKEIAinFR1L0WZSM0uwAUfSiSUdJv2oxVfESLm8OJRr/zIx7az2cMKDCQQGsSoAC0Na8re0UCJJAjAfHqXvGCg8mVOAaIrEGWwYXSmwmZa4tJWDAeGhr4ih4XIHgL5pPlanFZEs+lFYCiIPHqHaof7cELoEszLIsAFOWLjToPWpzBPqY5luW6HoS0cQEvYxBKpykPacRepCfL2EPpoa3qChAaB1+O09Z1zjnnuIrUcVkn5pEFBU7kKQCFoEWW9QdqCB9m6UvRv6vBgweHrm3U9RJbiJ6YSwilh1ZsMET9plwfCey8WTRATb6ifjPjx4/3xJlQqL9RfILxffr0MU10bqM0fO1ycP1NyDJuTB4Ioezy0u5D69d1PyuaiWl30tYl/MG92TV+ovqMexu0X9MGfMCJKgvPJWhV5xlqEYCiHTvttFNke+MEoG+88UasACXIANzFeVaoLtwvXKHsGqBFjKMinouu3wCuTVECUFzLou61KLsVBKBFMooSgIr5oNBvL/gbNceYp15++eVopjPwGePEwkgSaAkCtAEqd0IGEiABEogjAOcewSBLr4NR/rFoOqmVV17ZP47akQmY2n///bXXeDgRsQNsGcERR1SArcWgDVA4c3E5bYoqo9b4KDugsLsZZT8tS51gA6/SWQzUw0afLLPT7OTrf2x1osGj4Jgojd02XB/YPhQNt9gy7ZOrr766kmWz2gamHR+3D6c8w4cP13as4tKV9RzaD/uqsAkYZVuz2rYX/bsSobM6++yzYx2noH+w5XrFFVco/H6DAQ7PGhmK+s3ApifueSLgzdQ98BItOnXVVVfF5oN9TXgZjwuwW1lLgFdw3Bvko0TqYtDv++67z3k/K5pJ6kY6EuJ+hedHz549HWcro0Swre9Tu+22W+WJmCOXMySTvF+/fk6nVuZ8I7bnn3++Eo3tzFXD/i1spaZxpgM7x2IaRjsizFxRSTMUMY5a5blY1L22pEOhqmbVmxHmaLj/JAU4OIMjvP79+yclzXS+LT1jMoFhYhIoGQEKQEt2QdgcEiCB8hH429/+FmrUE088EYozEXAAIbYntcF1WS5mov0thAIQkMoyQQVPvLL0UImWqX/e7IhWlNkNbV0CWDi8qGfo0aOHs7oowagzcUIkHCeMGzdOiQ1Ate6660am7tChg+rbt68S26AqygC/KzMEVigbghGXl1q8+EKgB4cv1fAVDVMlGoBKzB4osUPoaoKOw4smBLHvvvuuGjRoUGS6Mp2AgBljFx5QIRg+7bTTFBwiQWC02mqr5d7Uon9XEGiK9pb2/i1287RDMbE5q1Av+oiXOXijhzMsCM/lM3ioj67feyhRwRFF/WbgwOjDDz/UDscwruMC7nG4b4oGnXZOBuc6cQGM8Rtz3QeRD7zz+LiDewPuEXDQE3TmY7cPTp/++c9/6ntP3O+2SCZ2e6rZh6AXAjmxV+oUXOP3CwdxGNNZnYBAyCBa3s5mZbn/OgsoIBLCddGqq6rkgQMHKrEhqMTGpzO/aLzqewIc8TX6A4izgTVGFjGOWuW5WNS9tsZLVqrs9WQEZ3qYN0fN5zDPwr0O9zyXI8w8wLWlZ0wevFgGCTSCwHTQY21ExayTBEiABJqJwPrrr6/EJqPfZLzoiBMelaRlKMuwldgv1IIhWUapBZ/iIER7+fYLq2IHGjhit9DPiYmfOPFQ0OZp5YA+Qsg2YcIEBS/REEDkpXEKbrhGY8aMUWLwX8nSdiWOXhI107LwxljAHzzrYmxAcAutOghfq9FQylJ3K6Ut6neVhZE4i1DwCG2H7t27a61iO67R+0X9ZqCF/vnnn/t/eLnEWMafmLuIFJAl8YA37bFjx+rfCYQvCy64oBY+QWsn74C6ZCm7rgta6+IwR2tsQ7BfTSiKSTVtsfNgqi+O3XQ/xWmPEtuVCtqNECxXE3D/EhvHSpzEVWTHhz1cu1YN+ECF8YLVF2a8QJiXRkO0FZjkPY4Mk1Z5LhZ1rzWcWmGbByOsrNliiy1COMS8goKGsQm4H+O+N2nSJB2PuSLmyvUMbeUZU0+mrIsEaiVAAWitBJmfBEigTRAQD9squEQQ2jXilbnu/cdLCLROMLEyQWxexi6ZN+m4JQESqCRw9dVXKyyvFgdG/t+iiy6a+KK08cYbq8cff7yiMCz3Fnt0FXE8IIFWIyAO5rS5iGC/YEICWtQMJEACJFAUgbQC0KLqZ7kkQALNTYBL4Jv7+rH1JEACdSKw4447huzABYUfdWqKXipqCz9RbzXLs+vVXtZDAmUmABMFxx9/vP4NwXwDtAChzQhtzqjwzjvvaDunwfO29knwHI9JoBUIQKvq0ksvDXUFWpBp7O+FMjKCBEiABEiABEiABOpEgALQOoFmNSRAAs1NAAKRoJARy1//85//1L1jt956a0WdWHJalD2jiop4QAItSMBl2w+/azjgEi+xod/4c889p+1bYhm+HWD3MqstRTs/90mgbAS+//57rR0tXt3Vzz//rG2KimdwNWXKlFBT0ziyCmViBAmQAAmQAAmQAAnUkQAFoHWEzapIgASam8DQoUPVnHPO6Xfis88+Cy2B9U8WtDNt2jQFT5N2OOOMMxKX69rpuU8CJPA/ArDvG2XLd8CAAdquLmwbdu7cWcEOJQRAsFkXDHBiBZu0DCTQKgSefvppbacYNq9hjxUCfthgdgUsi2cgARIgARIgARIggTIToAC0zFeHbSMBEigVATjGgHdoO7iWAtrn894fNWqUmjx5sl8snPTstNNO/jF3SIAEshGAh+gRI0ZEZoIWHLyZwwHKjz/+6EwHb/HVepl2FshIEigBAThpQ0ha6QCnfC5N6hJ0gU0gARIgARIgARIgAZ8ABaA+Cu6QAAmQQDKBQw45RNsINCnvvvtu9fHHH5vDwrfnnHNORR3nnntuxTEPSIAEshPo27evOu200yI1QeNKhN3Pe++9N2QjOC4Pz5FAMxAwAtC4tm699dbaVERcGp4jARIgARIgARIggTIQoAC0DFeBbSABEmgaAlgKeNZZZ/nthWbM8OHD/eMid0aPHq1efvllv4o+ffqotdde2z/mDgmQQPUEjjzySDVhwgR1wgknqPbt2ycWtNxyy2mP7/hNQhObgQRajQB+B3jmucLSSy+tLrroInX77bfTBIsLEONIgAQKIQCb/Isuumjob8YZZyykPhZKAiTQWgSm8yS0VpfYGxIgARIongDscP7++++6ojnmmEPtvvvuhVcK5yvjxo3z6+nVq5daZJFF/GPukAAJ5EcAy90nTZqkvvzyS/33559/qrnmmktresIm6HzzzZdfZSyJBEpKALauP//8czVx4kT9zINW6GKLLaY6deqk4PiLgQRIgARIgARIgASahQAFoM1ypdhOEiABEiABEiABEiABEiABEiABEiABEiABEiCBzAT46TYzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOgADQzMmYgARIgARIgARIgARIgARIgARIgARIgARIgARJoFgIUgDbLlWI7SYAESIAESIAESIAESIAESIAESIAESIAESIAEMhOYIXMOZiABEiiMwJQpU9TAgQND5a+88srq0EMPDcVHRey7775q6tSpFacXW2wxdcopp1TEFXnw7bffqiFDhoSqGDBggOrWrVsovpqIa6+9Vj3xxBMVWeeZZx51/vnnV8TxoFgCWa51lrSvvPKKGj58eKjxp59+ulp44YVD8Y2OyNK3LGm/+uorNXTo0FD3DjjgANW1a9dQPCOyEeB9JBsvpiYBEiCBshG47rrrQvPedu3aqZ122qlsTWV7CiDw/PPPq8suuyxU8rnnnqvmm2++UDwjykOAc7DyXIu20pLpPAltpbPsJwmUncAPP/ygIMALhi233FLdf//9wejI49lnn1399ttvFee7dOmixo0bVxFX5MGnn36qOnbsGKpi5MiRapdddgnFVxMBYXFwwrPIIouoL774opri2kSef//73+rKK69U77//vsLEMI+Q5VpnSTtq1Ci1ww47hJr49ttvq86dO4fiGx2RpW9Z0n7wwQdqmWWWCXXvlltu4ctdiEr2CN5HsjNjDhIgARIoEwHMnTGHtsOyyy6r3n33XTuK+y1KAO8Wu+22W6h3mGstvvjioXhGlIcA52DluRZtpSVcAt9WrjT7SQIk0OYJPP3002r11VfXWsbQQGQgARIgARIgARIgARIgARIgARIggbZAgALQtnCV2UcSIIE2TWDChAlq5513Vj169KirFnCbhs7OkwAJkAAJkAAJkAAJkAAJkAAJlIYAbYCW5lKwISRAAiRQDIGNNtpIffjhh8UUzlJJgARIgARIgARIgARIgARIgARIoOQEKAAt+QVi80iABEigVgL/+te/QkVMN910obh6RMBOl8v2KJbmt6VADm3parOvJEACJEACJEACJEACJEACjSZAAWijrwDrJwESIIE2RGCuueZSgwcPbkM9dneVHNxcGEsCJEACJEACJEACJEACJEACRRCgDdAiqLJMEmgRAv/5z38UPFA30mHOlClT1HvvvafgvbxsAZqVsK/5zjvvqGnTppWteWxPFQTKMOaraLafxfM8/Zv95Zdf/Lg8dooe60WXX+b7SB7Xh2WQAAmQAAk0hkDRz6/G9Oq/tRY9J8LcHnPoH3/8sW7d/P7779XHH3+sMF/KK7TaGPjhhx/Um2++qbDNI3AOlgdFlpEXAWqA5kWS5ZBAixCAsPGiiy5Sr7/+uho7dqzCQwthkUUWUSuvvLJaZZVV1AEHHKAWXnjhwnr8wgsvqEsuuUS99tpr6t1331WYgM0xxxxqrbXWUj179lSHH364mmmmmQqrP67g999/X1199dXq1ltv1cJPI5hFezp37qxWWmkl1a1bN7XvvvuqGWZIvsW+8cYb6pBDDglVef7556suXbqE4k3Errvuqr788ktzqLebb765Gjp0qN5/4IEH1Nlnn633v/rqq4p0OHjkkUc0S3MC+ZC/6IC29OvXL1TN0UcfrTbeeONQfJYIjItXX301lOWvf/2rGjFihGrXrl3oHCIaMebz5PDHH38ojJenn35avfjii/pF4i9/+Ytadtll1ZprrqkGDhyo1l57bWff4yLzHuvBuoouv8z3kSALHpMACZAACTQPgbyeX8ccc4zCs8oOW265pTrssMPsKH8/au6AcmDv3RXuuusuNXz48IpTs846q7rnnnvU9NNPXxGPg7zmRI8++qg67bTTKsqHuaOzzjpLz58x78Rc1AjZll56aTVo0CD9joE5TJ7hlVdeURdffLFmjWsH4SfmhKuuuqraYIMN1BFHHKHAJEvIawyYOot6HzDlf/bZZ2rPPfc0h3qL1UgYHwjffPONuuCCC9TIkSMV0prQsWNHtdpqq6n999+/4r3BnI/acg4WRYbxDScgNwAGEiCBkhCQr5L4HBn6k8lQphbONttsoTJEmJZYxpVXXum58gbbNO+883p33313bHmffPJJqA0oRx6skflEmOideuqpnggOnXlNO7p27ep9/vnn3oABA0LpRFAbWX4tJ37++Wevb9++ofpMm4JbmVR548aNS6xShFbOMp955pnYvEsuuWQon0xs/Dy4lsE2xR2LUNfPm3Uny7XOkvaOO+5w9uHtt98ONfH44493psV4fu6550LpTUSjxnwWDjLJdvbtlltu8UQD2ZMXCud5c71FOO/JxN90OXFb1Fg3FRddfpnvI4YBtyRAAiRAAvkRkA+doeegfATMr4L/Lynv55cIJkPt7tSpU2S7MYc2z3Z7K4oJkXn+9re/hfJsu+22zvR5zoluuOGGUL3yoVvP3xdddNHQOfRn+eWXd7YrKdJVF8r79NNPvSuuuMKbeeaZnfUZhiussIInGo9J1ejzeY8BU2lR7wOm/LfeeivEQOzR69MiDPfat28fOm/4YCtCaU8E7Z6seDNFOrecgzmxMLJEBPL9vCK/DgYSIIHmI/D777+rHXfcUWst/vbbb4kd+O6771SvXr3UgQceqP7888/E9GkSYPkINBDxFRv7cWHMmDH6ayQ0ROsRsAwEX61vvvnm1NVBgxbad6eccorWYE2dkQkzE4BG7oknnhjKB63cO++8U2vkBk+WYcwH25T1GBqfGJcurVe7LPxGobUNrYukUPRYL7r8Mt9HktjzPAmQAAmQQHkJFPH8wlw6GD766CMF7UJXeOyxx1zR6sknn3TG4/nvyrP99ttXpK/XnAirpnbbbTf1xRdfVNRvDnbZZRezm8sWc5/+/furqVOnxpYnwkE9ZzfakFGJixgDUXXVK16UDZQIydXkyZNjq8RqPFFS0e9pUQk5B4siw/gyEUhen1mm1rItJNBGCUycOFHJ183UvU8SIAYLgmAED8CsActJsHxi2LBhWbOG0stXZ4XlMmkDlmrgLxjkA1Mwqqbjn376SW299dYVy0HSFoiJ57HHHqvmnntuvawnbT6mS0/g4YcfVvvtt18oA5Z13XTTTWrTTTcNnUNEGca8s2EZImEmIks499xz1cEHHxxpPqLosV50+WBR1vtIluvEtCRAAiRAAuUiUNTza7HFFnN+yHzwwQfVMsssE4Lw+OOPh+IQIStj1Ndff60WWGCBivOjR49WQZvgMM+0zTbbVKSr15xIVjdF2vSfbrrpnCaSKhqa8eD+++9PnQNCYCzz3mSTTdTss88eylfUGAhVVMcI0WbVzCGYThsuvfRSBbNVeLcJBs7BgkR4XEYC1AAt41Vhm0ggQECWUqvdd9899V8WrUxZSq7OOOOMQI1KyTJ3dd111ymch8Hw++67zzkZO/PMMxXaV0vA5OyEE05wFgHbhQ899JBuA4yWX3PNNbptzsQFREK4ZtvCMVVgota7d29tVwnCYwg6ZQmNOV2xxURBluFUxBV9AFtKZsy4JnKyhN4/j3RLLbVU0U3KvXxo2UJzOSjwx7WRJU9qhx12cNZZhjHvbFiVkRDQw2YTbIPBmQBsOM0yyyyh0nA+7kNH0WO96PLLfB8JXQxGkAAJkAAJNA2BIp9f0L4LBghAgwFOSTF/iQpPPfVU6BTswQdDjx49lCx99qNrxcy9AABAAElEQVTrOScKCtpg6x728zFvg/38JZZYwm9Xnjs77bSTMsJgaHvCRr4siw9VMWnSJIX3Glcocgy46qtHHObP5p0RPhYuv/xyBaWb8ePH65VVuC7BgLkW5pzBwDlYkAiPS0ugRMvx2RQSaPMEomyAyg0k1i5LmvNRNkB33nnnUNmi1ant5gQvyK+//uqJYC2UXpaXBJN6WWwcivAwVCb6JMItT5atOMuOsh8kzplC6auNgO1IF1vYmrr33ntDxYozGk8mSM488kU5lB4RRdv8QR0yoQy1SYSeOJVLyHKts6QVYV2o3bgesAEKu04LLbSQ87xoOsb2qwxjPguHKBugYAF7WjKBDfVXnD452QwZMiSUFhFFj/Wiy0cfynofQdsYSIAESIAEiiNQpA3Qop9fLtuM8hHTEyekFcBE8875XDfzVMw/gwF2UM15s5XVIxXJipoTRdnlRDs222wzT5b6++0QgZsn2q3+cdaduLowN3CFl156yRPHRyE+sB0vTkYrshQ9BlBZ0e8DrnFmxoQ40PJguzMYjjrqqBAf5IEPhmDgHCxIhMdlJQAvaAwkQAIlIVBvAah86XQ+2E4//fRIIjCUbR6YZguBKYR/dsgi4IFw1pRltpj8iY0gu8iK/QsvvDCUB3nzFIDutddezjow0YoL6623njOfLPEPZSt6woMKW00AiokoDOWbsWJvjzvuuBBjO6IsYz7L7yNKAAqD9WIGwu6evy+2mpwOzUQLwk9j7xQ91osuH30p633E5sx9EiABEiCB/AkUKQCtx/NLlruH5jSyfLsClKxqCaWx5z8oww4ffvhhKD0c2WAeZEKRc6IooSSEjuJLwDQhl21UXZj/ioZjZB2y5D3ECEyDDlvrMQaKfh+IEoBiLikrhJyMIITHmLHHGfbhWCsYOAcLEuFxWQlwCbz8ihlIoK0SeO+995xdF6/zznhEwjaOPAwrzsOGDJZLVBuwtD0Y+vXrp8SjezDaP5bJiLY/6kck7GD5L5ahp/lDUSLQVeJlO1TqKqusopKMtEc5m0lyVhOqjBFOAliyD3tXwSBfpJ3OkOx0ZRnzdpuq3RftTzXffPM5s2PZkkz8Q+dg4yoYih7rRZdv+lOP+4ipi1sSIAESIIHWJ1Cv55drGby9fB0OaIKOjoKmbuA4STQX/Yti5zeR66yzjlpwwQXNoWrEnGi77barWILvN6aAnUGDBqkZZ5wxsuTBgwc7z8ERlQn1GgOmvnpvt9hii5DtWNMG0YZViy++uDn0t665JOdgPh7ulJwAnSCV/AKxeSQAAjCEvscee6SGAXua06ZNS0wf5WUSNmGizqFQWXqsbcTYFcAO6BprrGFHpdqHIyNZWh9KKxp+oTg7Ag/lTp06KdiBTBP69Omj5OtqmqTaWyQmP/LlM5Qe9hZdNnHshLBbCsHUt99+a0fXJCSuKKiNH9gTUxsF7A8lhahxXc8xn9TGtOdXXnnl2KT4jQQDJvLBUPRYL7p89Kde95EgOx6TAAmQAAm0LoF6PL9ADwLQoD1+2w7o2LFjtS18mzQc9sC5oR0gJDUf6V0OgILe3xsxJ4Ktz3oF2BeNC7B/LxqpKijQs4V59RoDce0s8lzS+5bL2VHwHZNzsCKvEMvOmwAFoHkTZXkkUAABPKDhSCdtOPXUU1MJQGFQ3RVWW201V3RsXFRZsZnkJCYWruD64hhMBw3RtALQYN6kY2iKugKErmkC0tUiAJVlA7HVQBuAoZIAvL4feOCBCgLoqBA1Tus55qPaljW+Xbt2sVlcBv5d46bosV50+YBQ1vtI7AXiSRIgARIggVITqMfzCwDWWmstJSacKjQ4IYSDgBJKEC7v72LTW8nSb/0B0EA0AlB8wHc5RQoKQBsxJ4Ln+3qFNO8S4B6cQ9jH9RoDUUyKfh8Q8xFRVev4oKaxK7HNyz6fhn+R73J2W7hPAoZA5TpWE8stCZBAmyAg9oFy66dLWzJN4fA26ApiV9QVXRFnL+OpOJHDgdgncpbSoUMHZ3ww0pUuaqIZzIvjpAlP0JOmq4y2FgdmBx98cCy7Moz5vK7LnHPOGVtU3LIvO2PRY73o8tGXst5HbM7cJwESIAESaC4C9Xh+gQhWFmFpeDAYLdDHHnus4hQ0GyE4Euc1FfFmmfwTTzyhVzPZJ1ddddWQaZxGzImiTPfYbc1rf4455kgsCqvaguHrr7/2o+o1BvwKAztFvw9AAzYuuD6mB9NzDhYkwuMyE6AAtMxXh20jgYIJzD777LnV8Ntvv1VVlmvigYKC2pOuwn/88UdXdC5xCyywgLOcNO1CxsmTJ4fyp/kSajIlCThdS5lN3raynXfeeUNdFa+e6vrrrw/Fm4gyjHnTllq3008/fa1F6PxFj/Wiy0cnynofyeUCsRASIAESIIGGEKjH88t0zGUHFAJQceKjnn32WZNMb2EDHAF2+e0ArdEJEyYo1/J3caJkJ9X7jZgTpRFKhhpaZYRrLh4syvUuseiii/rJ6jkG/EqtnaLfB5IEoElmv9BUzsGsC8bd0hPgEvjSXyI2kASKI7DkkkuGCsdSh7S2Mu3M1X7RxfJ+V0gjaMQkr6iAJTGuELXMI5jWth9kzq200kpmN3E7derU2DRp7F3GFtDkJ7EkDMu74LALtrHscNRRRylM9F2T7DKMebutZdgveqwXXT4YlvU+UobryzaQAAmQAAlUR6Aezy/Tsu7duyssR/7hhx9MlJ6PY/l7UMnAaH4aQaifQXag/elygBRc/o48jZgTpVlSbfenln1bkzOqnC+++CJ0yuZSzzEQaohEFP0+EHRs62pDUhznYEmEeL5MBCgALdPVYFtIoM4EOnbsGKoRmoVYVoO/eoT27dsrLOUNCvRefvnl2OphgDvL0h3YUN1nn31iyzQnZ5hhBoUl7PjqGVx68uKLL5pkkVsIZl3LQVwC0CgtPpdjKFMhvlbXqgHqsgVpyi/7Ft7N8UKAL87nnHNOaAnYpEmT1LBhw/RfsC9lGPPBNjX6uOixXnT54Fev+0ijrxXrJwESIAESqB+Bejy/TG9gtgaONmHX0wTM9Y499lhzqLeYN/bo0UPvw54mPgjbzowuuugi9fnnn1fkWW655ZTLIVAj5kRR896KBud0AIWOrbbaKrI0LG93aYDa9v7rNQaiuBT9PhAJJ8MJzsEywGLShhP4S8NbwAaQAAk0jEDUFzuX4XTTSAgdTzzxRHX77berd955R8F7dq3BnmiYsu66667QF29zDttrr7025BHTPh/c33TTTdWuu+6a6g9fQ+FgBl/jg+Hee+9VL7zwQjC64vi4444LCU6RoEuXLhXpcBBlfBxCvKgQXApl0gWFtSbeNanK47qZ8uu9hbMjszypZ8+e+oUh2AZ4RnVp4ZZlzAfb28jjosd60eUbdvW4j5i6uCUBEiABEmh9AvV6fhmSrmXwr776qjmtt2ussYayPXMHl8EH0yOTS/sT8Y2YE7nmpGhLEQEmkYIey+16Lr74YvvQ37c1QOs1BurxPuB3sIAdzsEKgMoiCyFAAWghWFkoCTQHgW7duil8FQ4GaEsGl9uYNOedd5464YQTVO/evdXyyy+vYD8Imo2ffPKJSZJ56/o6iy+ee+21l3LZvvnqq68UPN0XHXbffXdnFQMGDFCuJTNIfMstt1R8vTcFQPiJ5drBMM888wSj9DGEfFF932+//Zx5oiJdznDivihHlVOWeExG7XDWWWcpaO3aAUuGDjvsMDtK75dlzIca1uCIosd60eUDX1nvIw2+tKyeBEiABEigBgL1eH6Z5m222WYqySajWf5u8riWwZtzZhslAG3EnCiNTUnT7lq3WAL/97//3Tmfxjz+ggsuCFWB9xqjYWtO1mMM1ON9wPSniC3nYEVQZZmFEBCNIQYSIIGSEPj+++89+aGH/kRwlqmFs802W6gMEcA5y7juuutCadGGddZZxxN7l34eEYh6l112mScTl1D6DTfc0E9ndkQgGkqHckeOHGmS+Nuff/7Zk+UTzvQ77rijJ8vOPfmC6/3++++eGIT3ZHm+My3KF1s9frm17qBdUXWJAx7v0ksv9d58801P7DV54nnT23fffZ3tkq/dnizpdzZHlqJH9n3IkCGeLM3R+cQIvvfoo496Xbt2ddaBvsskz1mHaAuE8sgE27v77rt1+3Fdn3/+eWfeNJFZrnWWtHfccUeo3ejn22+/HWrWAQcc4EwrS+VDacsw5rNwkKVtzr6JsD3UNztCJvChfPLiZCfx94se60WXj46U9T7iQ+YOCZAACZBAIQREey70vJtpppm8FVdcsaq/cePG+e2sx/PLr0x2xBt8qC+Y+5g/sfFpJ9fzRMwzzfngVhxwVqQPHhQ1J5Kl/M42ffrpp8Em1HwcVZdhIQJgPc817xLiJMoT50bO9skqrlB76jEGin4feOutt5z9HTVqVKi/dkTauSTnYDY17peZAJZpMpAACZSEQCMEoJgMiA0g50NRNAe9VVdd1YMAVr6IOtNgciFLskMEswh4kPmSSy6JLB91iEMbD+0xk5mobZ4CULRr9OjRnmgXJtYb1R7EH3744SgqMsjS/MjyUfeaa67pyXKnijQuQXSUAFQcAlXkdbX15JNPjmxf0oks1zpL2iwC0G+++SbECP0U7WRPlvtXdKEMYz4Lh3oIQAGo6LFedPnoQ1nvI2gbAwmQAAmQQDEEXAJQ11wnbZyYOqpoaD2eX6bCKIEk2o6P12IX1CT1t2uvvXbkPG/w4MF+OtdOUXOiKKFkIwSg5rrjXQKCcXMc3EIoKj4JXJgKnyOh0iLfB4oWgKL9nIOBAkPZCXAJvNz5GEigLRPA0uHbbrutwp6Q4SGTIvX6669rb5JTpkwx0RVbOKFZd911K+KqORDtSSUTuMisWLKN9pgAO51Yqh8MeS+tWW+99fQSmWptFvXq1UvbTA220z4eOnSoci1TRxrY6oRDqJ9++snPgjaJdqh/bHai+h619MnkwzZqSb+dpsz78803n3M8jB8/Xl1xxRUVTS/LmK9oVAkOih7rRZcPhGW9j5Tg8rIJJEACJEACVRKox/PLNA2OkDBPcQW0Y+aZZw6dCtoBtRMkzQFbdU4EVqK9aKNQeJeQFVUVceYA7xUwqSRCUhNVsa3HGCj6faCiQwUccA5WAFQWmTsBCkBzR8oCSaD5CKy++urqoYceUi5vkFG9wURh+PDhTkFcVJ64eEzA4HwpjX3LWWaZRY0YMSLSqHtcPdWc23///dUzzzyjXAa+o8qDnUr5iq/gzCnJnhNsqMIOURohK2yvPvbYY0qW4EdVHYqX5VTaY3rohBXR7AJQdOXggw9W8BAfDPCgKmYKKqLLMOYrGlSSg6LHetHll/k+UpJLzGaQAAmQAAlUQaDo55dpEmxBbrDBBuawYhu0/2lORtkBFY1GBTufSaEV50R4V8AcHM4yk8L888+v34N222232KRFj4Gi3wdiO5fDSc7BcoDIIgonQAFo4YhZAQk0BwFoX8ryCHXSSSeppZdeOrLRmFBASPnuu++qQYMGRaar5gS+aos9Si04dHmmhIAQRrahlSrLvaupouo8mECKTSjdvjiN1w4dOqi+ffsqsQ2qooymuxoxcOBAJTY+ldj4dJ1WsrRfO36CkyXX139npv+PFJuwmtmmm27qTIYJi9hgdZ5rpkhwOf3000NN/u6779QJ4rgrGMow5oNtKsNx0WO96PLLfB8pw/VlG0iABEiABKojUPTzy7TK5Q0e56IEnWK336m5iA/gUFhIE1pxTiTmo7TSwIUXXuj8QA4FBTgofe2111ScFq3Nr+gxUOT7gN2PovY5ByuKLMvNi8B0WKOfV2EshwRIoHUIvPfeewp/8KAIj/AQ7EG7DoLJueaaqy4dheBqzJgxSpwxKXFGpDDBW2ihhepSd1IlEydOVGKbUU2YMEEvqVlhhRUUPL1HeXFMKs8+D+Eylm5/+eWXurwll1xSmwdIoyFql+Pa/+qrr9TYsWP1tcXEcMEFF9RCV7Gf5UrepuLKMObLCLzIsY7+Fl1+me8jZbzebBMJkAAJkEA6AkU/v9K1ophUrTYngshDnKrq+S+WwYt9fQWNSygB1BKKHANFvg/U0ucseTkHy0KLaetBgALQelBmHSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAg0hkE4nviFNY6UkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkUBsBCkBr48fcJEACJEACJEACJEACJEACJEACJEACJEACJEACJSZAAWiJLw6bRgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkUBsBCkBr48fcJEACJEACJEACJEACJEACJEACJEACJEACJEACJSZAAWidLs6oUaNU+/bt1fLLL5+pxqlTp6rLL79c7bfffqpnz56qY8eOaq211lK77767GjZsmJo0aVKm8mpJfOGFF6pVVllF/73yyiu1FMW8JEACJEACJEACJEACJEACJEACJEACJEACJFAXAvQCXwfM33zzjVpppZXU119/reaff341efLkVLXedttt6ogjjlCffPJJZPo555xTnXjiiWrQoEFqhhlmiExX64mxY8eqrl27qj///FMX9eSTT6oePXrUWizzkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEChBIqTmBXa7OYp/IcfflCbbLKJFn5mafU111yj9tprLz/LrLPOqlZccUW11FJLqS+//FK98cYbCmX/8ssvasiQIerVV19VI0eO9NPnufPHH3+oXXbZxRd+5lk2yyIBEiABEiABEiABEiABEiABEiABEiABEiCBIglwCXyBdN9//3210UYbqXHjxmWq5d1339UanSZT7969tRboSy+9pG666Sb11FNPqS+++EIdfvjharrpptPJbrzxRvXPf/7TZMl1O3ToUPX222/nWiYLIwESIAESIAESIAESIAESIAESIAESIAESIIF6EKAAtADK//73v9WZZ56pVl55ZfX6669nruHoo49WU6ZM0fmgPXrrrbeqBRZYoKKc2WabTdcxePBgP/6ggw7y9/Paefjhh9VFF12UV3EshwRIgARIgARIgARIgARIgARIgARIgARIgATqSoAC0JxxY2k6bGXCdieWjiMstthiqWv5z3/+ozU8TYbhw4ebXef21FNPVbPMMos+B1ujH3/8sTNdNZHffvut2nPPPZXneapTp05queWWq6YY5iEBEiABEiABEiABEiABEiABEiABEiABEiCBhhGgADRH9G+99ZZaY401tD1OU+wBBxygHnroIXOYuB0/fry27YmE7dq1SxQ6QvgJ26AmVKNxavIGt/3799de5qeffnp1/fXXqznmmCOYhMckQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkUGoCdIKU4+X5+eef1bRp03SJiyyyiLr44otVr169Yr24B6vHUnfY+YSjIwge04TffvvNTzbjjDP6+7XsjBgxQt155526CGizduvWrZbimJcESIAESIAESIAEUhO46667MtkfX3LJJVWfPn1iy//999+1w8jXXntNjR07Vjuo7Nixo/6QPGDAANW5c+fY/OZkEW2DfXd8bP7ggw/UZ599puaZZx79Ebxv375qm222UX/5S3qdhTzLQp/zLC/Pssz14JYESIAESIAESIAE0hCYTpY3e2kSMk0ygRdeeEFPvo888kjtwX3mmWfWmT755BOFiTnC/PPPryZPnqz38/j3008/6Ukyls4jQHC60EIL1VT0Rx99pFZZZRX166+/qlVXXVWNGTNGQbC65pprqldeeUWX/eSTT6oePXrUVA8zkwAJkAAJkAAJkICLQHBFjSuNHbfpppsq2C2PCi+//LLafffdFRxNugKcSu64447qqquuUnPNNZcriR+XZ9tgbgjtevDBB/3ygztwhjly5Eg100wzBU9VHOdZFgrOs7w8y6roNA9IgARIgARIgARIICUBaoCmBJUmWZcuXdSHH36ohYVp0ueR5vTTT1dG+LnooovWLPyEA6ddd91VCz+xvB4T7ry0SvPoL8sgARIgARIgARJobQL/+te/1JtvvplbJzGXgU1zlIsAYefSSy+tP/biI/W4cePUn3/+qW677Tb1/vvva2Fk1MfkPNsGjVRod7744ot+X2FvfaWVVtLmlIxdd7QLq4xGjRql4ATTFfIsC+XnWV6eZbn6zjgSIAESIAESIAESSEOAAtA0lFKmmX322VOmzCcZtBjOO+88v7CBAwf6+9XunHLKKf5E/LTTTlPLL7986qKgMfr111+nTm8nhOAVk/siAgTEcEgFkwIzzDCDXkaGlx8GEshCwFaW5/jJQo5pSYAEmpnAvPPOq7BMvJ7hnXfeUVOnTtVVYgXNQQcdlFj94osv7kzz1VdfqUGDBvnCzyWWWELdeuutelWLyfD555/rFTzPP/+8FoZi2TmWartCnm07+OCD/TlX+/bt1f33369tyZt6sbIIAtLvvvtOa7dinnfdddeZ0xXbPMtCwXmWl2dZFZ1OcQCBNcwdMJBAWQlwflnWK8N2kUDrE8D9x/6D3AR/iDNbfHjNYoYnLbU555xTLbvssmmT55dOOsdQMAH5gg8zA/pPlsDnUtvEiRM9mez75crg8cQWaE1ly0TbEyGhLrNnz56eDPqK8mTJl1+fLIGvOIeDm2++2T9v+pt2Ky84VedNWwfT/XcMkgM5cAxwDHAMcAykHQMdOnQIPe+LjhBbmP6cYJ999qmpup133tkvS+yzeyJMdJYnNtw9Mf/jp73xxhud6fJqm9j59GSFja5PlrZ7Ilh11ieasN7cc8/tp5MPzaF0eZaFwvMsL8+yQh1PESGmD/xrmnbMMx3vjxwDHAMcAxwDHAPFjgFxsJ3iKZ5/EmqAyshutvDDDz+ozTbbTBvJR9uheXrHHXeoWWedtequQHsTS9+hiQnv89dee61eIpalwGWWWSaVloarzEmTJiks9dp6660rvNq70maNgwaFCIxjs0Gj769//au2p2q28HpPTb9YbG3q5H333ecvyYS9toUXXrhN9Z+dJQESaHsEzj333IZ02tbYW3311atuAzQnb7nlFj//Oeeco5/zfoS1gxUiF1xwgerevbuOPeOMM1S/fv2sFP/dzatt559/vu84c6+99tIOj0KVScQKK6ygtTFPOukkvUwfNkqPPvroiqR5loWC8ywvz7IqOp3xYO2116bt+ozMmLw+BJ555hkF7XMEOM9N64ytPq1jLSRAAmUiAK1MrJCBaRmscMXWtQ+zPnkF+ITp1KlTXsXpci655BLfjGOuBacojE6QUkCqNUmeTpAmTJigtthiC98zKlSS7733XiUamzU1UzQsFDy/I8BW1i677BIqr0gnSFdccYXab7/9IusONSZDxN57762++eabDDn+mxRsoZYNwa75Ey2MzOUwQ2sQ+Pvf/+4vPYS9tq5du7ZGx9gLEiABEogggGce/jD3qGfAnAbOFhHgvAhOh6oJo0ePVhtssIHOCpuecBSZFERLVKfDB1Asn8fSdDvk1TYINt9++21d9OOPPx47j8Oye2OSSDRyFeaVMOtjQp5locw8y8uzLNPfLFs478T8FQ5KYdqJgQTKRuC4445TJ598sm4WzHPA6RkDCZBA2yIgeo7aHCA+3H7//ff6z943cT/++KNenl5POjvttJNWlMuzTpgjQv9++eWXPItNVRY1QB2YzjrrrER7lNDAXG+99Ry5i4t6/fXX1VZbbaWgLYmQl/Dzzjvv9IWfGOAu4WdxvSq2ZNwkqhF+olViUkCBOf5MmG+++bSWhhGIwjYZnEUxkAAJkAAJkAAJ5EPAaFnCCSMcAlUbjIAR+Y0AMaksPN8hKMXLCASTsAdqhzza9tNPPykINRHg2X3ddde1qwjtQyNMTCjp+QzslcJRk9ESy7MsVJxneXmWFYLCCBIgARIgARJoAgJYaWsEmGZrhJtmixW+WIlbxoA2t1KgANRxNeFYyAgZHad11FxzzVVXAehDDz2kvwjiB4SAiTA0P2vVQkM/+/fvr8vEkt5LL71U77fKP7wk5Bm+/fZb9eyzz+o/lAsNETiHgDdZIxSFdkYRhoLz7AfLIgESIAESIIEyEoC2KV4EEFZccUU188wz66XiEBjib8qUKapLly76XNIHSFtzFdoGaQI+dJowfvz4CgFoXm2DVisErAhoF/qYFDDPMB90Idg1AtA8y0Ib8iwvz7KS+PA8CZAACZAACdSTAJagGwGmEWzaW3Muz+Xo9eyfqYsCUEOC27oRgL0neP6EJ0sETIIffPDBXGwxwN4VhHoI0EKABmhUsIWJhx56qLaZibTQSh08eHBUtobG220uoiF4gYHtUvw9/PDDugq8yNgCUQhG7ReqItrBMkmABEiABEigFQgYDUv0Bdqf+DB76qmnhmx5w2bnDjvsoC666KLIZ6ztvR6ak2kCnucmYBWJHfJq2xdffOEXm1Ywu9hii/l2Aj/44AM/f55lodA8y8uzLL/D3CEBEiABEiCBAglAYInnvxFgQgBo75tj2N5sCwF9b6VADVDH1YQtyp9//tlx5n9R1dqj+l8J6fb+8Y9/6Im/Sd2tWzd19913R072Tbq0WxjRNeHTTz9V+EsTXnvtNT8ZloGXNRQtAHX1G0zFa6vvMAdpYENtueWW0zZFIRyFIWE4WWJoHgKrrbaafhiixXCUxUACJEACJJA/AdvsDGySi9d1ZyX4KAwHR0899ZS6+uqr1ZZbbhlKZ7QkceKNN97QBvfjVmjgpccWLhpNVFNwXm2z55jzzjuvKT52O8888/jn7fz2fq1loYI8y8uzLL/z3CGBFiOA9wM4P0Kgg80Wu7jsTqkIYIk5BJtGS9Ml1MS5RtilLBWoQGPApJUCBaCOq3n88cc7Yusfdfjhh6uzzz7br3jnnXfW3tmTlnz5GbijBY240UHzo5F2NWAHa8yYMfoPlwVL5xdddFGtKQqBKJwtQQsEGi0M5SRw0EEHKfwxkAAJkAAJFEfA1rKEt9PZZ59de+/u0aOHtuOJD5swRXPHHXfoRnz99ddaE3TcuHHaFI3dMtj9hMAT5cChEbyOHnjggXaSin14LLdffOI0QGtpG+YEJsw666xmN3Zrz/1gBsCEPMtCmXmWl1dZEydOjBSEGw5R26AQOyod40mgUQT69eun8MdAAiRQHYEyOxCqrkeNzQU5xZxzzqnw4RV/+LgKxohvhUBpS0mv4rBhwyqEn1hyDudMeQ88eMQ88cQTU1HAy4fRfrjvvvvU+uuvr/PBSUFZwx577KHwZ7Q68OKEP2h4TJ48uWHNxk0EQln8PfHEE7odEH4utdRS/vJ5CEUXWGCB3K95wzrNikmABEiABEgggYAtAIU21COPPKK9kpts0PQ85JBDFGyj77777touJuxw7bPPPurpp5+ueGZCW/+AAw5QF154oc5+9NFH62X13bt3N8X52+eee06dcsop/jF2jOkhE5lX22zNyGoEoHDSaEKeZaHMPMvLqyzYXsW1qybAZj4DCZAACZBAcxJodgdCZaMOJ9oQaBrhpr018dhOP/30ZWt6bu2hADQ3lPkVBOP2J5xwgl/gkUceqSCoLCJAo8DWKoirw/4hQCMDjqCaJcC+6QorrFDxEgUtDzhUMELRjz76qELzo959w4vWu+++q/9M3eCMpTGwIwpNUfxhOT0DCZAACZAACbQigTPPPFPb1YbQ67DDDtOOBl393HzzzbVgs0+fPvr06NGj1Z133qm23377iuQQat5+++3auSWe+z179lQDBgxQyI9nK579jz76qC4LK0XwsRfL6hHatWunt+ZfXm2zPxwHhaymruDWXsViO03KsyzUmWd5eZUFZ1jQ+q0m4PriQzgDCZAACZBAeQi0FQdC9SKOeQE++tpCTFu4aTQ57flDvdpWtnooAC3bFZH27L///trjKZqGCTqM/9cSrrjiCm3rAmXAc6rLTlYt5TdrXqh2r7XWWvrP9AHLrIxAFNvPPvtMa4+a8/XeYpnbq6++qv9M3QsuuKAWhEJDFC9vcPLAm5mhwy0JkAAJkECZCGD1iq0J6GrbZpttptZbbz19qnfv3q4kzjiYBoIzxxdeeEGfh9fxoAAUH2tvvPFGhbTwoo6l61gKj79gOOOMMxScDRkBaPCDY15ts22A4yUwTbDT2e3Ksyy0I8/y8ioL87V11103DaZQGs6PQkgYQQIkQAKFEZg2bZqC6RHjKMjeYt/82SsZCmtMCxSMFaIQbBphphFwGoGmiYfSFEM6AhSApuOUWyr86OMCJulYwmUCNBow8cOXf/xh4o5l8NDGxB8mdrZB/5NOOkkv9zL5sT3nnHO0UA/7e+65ZyoB6Jdffqkee+wx9corr+g/2Ngy3uJRTquGRRZZROFvww031F0Ec2gP2ELRSZMmaTsYjWIAO2b4w9hAwPWHEBTCUPMH+6J5m0toVH9ZLwmQAAmQQPMSOO+887T2ZVwPIKQ0AtC4dK5z66yzji8AhQNCV8Azffz48XoO9OCDD4aSQNMTws/+/fvrrUkQ1AA18Wm3UW2zBYNpXwJtu5/2Cpw8y0K/8iwvz7LSMmc6EiABEiCB/Angndh2IBQUbBqHQrYd7fxb0Tol4v0dHzNtgWZQqIljPO/5Tp/vdacANF+eztJwQzABAsy4AI+mdrAnvHY8bkIIv//+ux2tbA2BihMZDvBysMsuu+ivN1HZLrroIoWJfat/WYeQ2QgVDQu8rLz33nsVQlHb0L9JV68txhSEtPgzL3Ywa2Dabba4iTKQAAmQAAmQQDMRcH2QxXMN5mEgsMRqCBPwsTIqwKb2Aw88oLVBr7rqKvXhhx9qzVQIOddcc01tcxvPU6z8MAErLuICNEXhpR52xZEPz1m0q2/fvmqbbbaJbBs+tJqAlScISWWBgwnQBjEhz7JQZp7l5VmW6S+3JEACJEAC+RFwORAyWppGqIljCD+RliGeAISVtgOhoFDTCDwx97CV2OJL5dk8CVAAmidNR1lQAd9tt90cZ9xRb731lvtEHWJxUzv55JO1UyQjqF188cX1EnHYqILjI6i1I8D7Kn7Al19+eR1aVq4qYDx41VVX1X+mZdCOhf1Ooyn68ccf5yKMNuVn3UIQ/sYbb+g/kxcvTMaeKF4eO3XqpNAXBhIgARIgARIoisB+++2XuAR+jTXWcFYf9UEWz1g4QcLH2G7duvl54TQpKuA5DadJ5kOhSYfl+bA3inkNlrjbK3Xssk16bKPKgmNDeKPHx2yUBTM7Jthtg3d6Ez755BO9MifYrmBZSGfCSiutZHZVnmWh0DzLy7Msv8PcIQESIAESSEUgzoGQ0eCErMIoVqUqtA0nSuNACAJPLFtnKC+B6UToRVF+QdcHwjAY5zee01ENvFEmeR+HIA0vA0b7E5NoeDCF9oIJ0EKEo6Szzz7b/xpz88036/pMmqxbaETsu+++Ohu+SJx77rl6Ob35EePmCO/08BpvbpSY5O+0005Zqwqlh51SvCSNHDlSa5+GEjRZBH5WeFmBVgi0RbHFy4wRLJehO/hC1aFDhwpNUQi8bWdXZWgn20ACJEACJNAYAliehT8ICOsRnnjiCe2wCGZ3oPH40ksv+c9N+4MsbH1+8cUXoSbBudGll14aisdqGTg/evHFF/1z+CAIQSLsbEOgagKejXiGYyUFNF7MShfTNpjBQTkwRWNCVFkwR2PaabcNH5WxPHzq1KmmCH8bVZZpFxwLQWhrHFjmWRYakWd5eZblA8q4A1NO0PAt0qFoxiYxOQmQAAnkQgDPEMgV8FzCH55L2OL5CQHnn3/+mUs9rV4InvNxDoSM1qaZD7Q6j3r0b4klllDQMG6EyQSKpwu4whAOwu7m8ccfX5UW4NFHH+0LPzfZZBN16623hlqJLxDwRoq6IKhEOOigg6oWgEKb4YgjjvDrgUBy77339o+xA8HYscceq18ITJ3QAM1DAFpRUQsc4EVlySWX1H9w7oCAhxSE4hCGGk1RcG9UwAseXmrxB3uvCDPNNJPWDDXL5rG1Be+NaivrJQESIAESaH0CWL0QFGDGfZDFh2D7w+IKK6xQAQmrVrp3765XQ5iPyu3bt1f333+//tBsEsOJEpasYzJu9AKCZn5cbUsqywg/UY/dNnxY3njjjXU7TBuSyjLtwgoUI/xE3jzLyru8vNtmWHFLAiRAAm2FALQ4jWDTFnIizjaz11Z4ZOknnkG2AyF7OboRaiKODoSyUG3+tBSA5nwNsex4r732qvDaDY+iabUnMJGHHSgThg8fbnadW3iIhydTTMzh3RRaDBC8ZQ033HCDv+wLk/Kg8NMu79BDD1VwaoDJ+LPPPqsl97B1wRBPAF+NoG1iL12D7dB33nnHF4zCJpl5SYsvrZiz+FKI9uDPBFxb43EeAtGll15a2zYx57klARIgARIggTwIQFgJDUdjbgdlxn2QxXwJmpkIyIcl7nZAHOZV5rmKlyE4moSWpR0g7MQS9LXXXtsXqJoVMSZdsG34KBxVFuKxkgdzMwS8YAXbttFGG/kCUHw0RV86d+5sqtNbtAtlrbzyyv7KG5cn+jzLQsV5lpdnWRVweEACJEACLUAA79PQ1gwKN40mZ1pHeS2AInUXohwI2QJO7NOBUGqkbSohBaA5Xm7Y78SE1564H3DAAXoZuW0HKa5KeCmFLQ4EGMcNTtKDeaEFsOKKK2pP7TiH5fbVCEBvuukmv2jYAY0LsGMFDQ1M2Oebbz5fWyIuD8+5CWBpIV648IeAhyA0Ruyl859++qlekuYuofhYqKZjCRn+TFhooYW0UBTCUAhHlxA1dmiPMpAACZAACZBAtQSghdGlSxf/IzJecqLsgz788MMKS+FNgKAT85JggBDUBJQP0y/BgOfc4MGDfeEnzts2N3GMvEsttZT/gRB1uZawIy2e40ZjE8ewJWp7bkecXT7SYlUPTBHZZmgQj/mWMTuEfK6X4TzLKnvb0D4GEiABEmgmApANYKk6tDaNNqe9tWUHzdSvvNuK52qcAyEj4KQDobzJt63yKADN8XrDJpO5gcHz5cUXX6x69epVMclNqg7LjSGMhO0OexIcl8+eDNsT/bg89jnckI1wC+rgXbt2tU8792GvkyF/Arjx4+UMf7BXhgAbWtAMNUJRLJ+HfTT75Sr/lsSXiK+S+IPGCgLGaseOHX2hKDRF8RtwvYzqDE34Dx8zYGcX4dFHH1Wrr756E/aCTSYBEiCBchOwl7RjH46EhgwZoh0Fwabmm2++qb25wwSPeQ7iYzHsk5tju4e2qRmsusC9GyaKsMW5xx9/XDstCjqhvOyyy7RpIHsuZrcNz+Y0bUNbxo4dq4WYdlmo1w7XXnutnvvtscce2mYlPmjfeOON6p577rGTaSHpMcccUzFHzLMsVJZneXmWVQGCByTQIgROO+00ddZZZ+neXHPNNfrdsUW61qa6gfdxI+C0hZt4X8IqTdfzqS0Bgvk+I8DE1v6zl6NjpQYDCRRJgCMsZ7pY7g5D61gGX42h3AUXXFD17ds3daswmYfTJBNg6D1rgNapCRtuuGFIaAXNA9zUuczdUKrvFg8CvNzhDzbKEGAPBs6VjC1RCEgxFhoVMEbQBvyZgAed0RA1S+dhh6VZA5ZQGu1svPgykAAJkAAJ5EsAH2SN40hof0LgCNMsp59+uv5z1QbhKLQnbeGiSYfnIp6VdsBxv3797Ch/H6ta8DzDvR6OC/GMNcvSURaO7ZDUtllnnVXBARM0QoNlGVMzeMZjBQXmWY888oj+s+sw+9BAxXPI1a68ykJd6Gde5eVZluHALQm0GgHcI8z8kk5ryn114RgvKNw0x7jftcUAeUecAyEj6LRtV7dFTuxzeQhQAJrjtcCyLQiAqtHCrLYZeCkwGgnQjMDS5KwB2hQmQGCFgOVgWHYFr+yYCEPgAw2/1VZbTUE7wQjiTD5u60sA3mOhvWJrIeIBbASi2GJJXNTyvHq0Fi9z48aN03+mvvnnn197nTeC0U6dOlU4czDpuCUBEiABEmh7BOwPstttt51ekTJs2LDQBz4IDDHnwrL1KGEm6GGJvNG6gXkg2KMcMWKEP28yhPGB98ADD1Swcb7tttuq559/Xp96++23fQGoXRaeYfvss49KahtWAiWVhXaNGjVK216HR/rgBzZ82IaAF1qfRZflYlamtpnrxS0JkAAJFEEAH8CwMsAINe0tVmc28r2qiP7GlYmPikZ4abZGU9NsEY+Pcwwk0EwEKADN8WrV+wYAzU84IzJh4MCBZjfT1nbQhBsZJvl9+vTRDpXsgiBQw98dd9yh+vfvr+uGlh9DOQhAexh/G2ywgW4QHuKwH2oLRYN2yerdciwBwd9zzz2nq8YS+cUXX7xCKAotamj+MJAACZAACbQtAvYHWdiXHjp0qILJHSwRveWWW7RDSXz0hS1yPDuSVqbgmWcC7HfCoRKWmpoVFNBcgTATqxTMfAbPICNohOkZE+yy8EEYbTvssMN0mzAfg2ka2GSHo0NjE/vee+9NLGsJsaEND/EvvviidpqED4f48IwP2mgb6sKzMk27ai0LfbX7WWt5eZZlrgO3JEACJFALAQgxcb+2hZvGtBdWIeD9qZUDniewoWkLMY2AE1sTTwdCrTwK2nbfKABt0uuPr1Cbb765/yUKLwrQhKgmQNvThO+++05tttlmeikGhFAoF95H8UB47bXXFFT/EfASgQk/bEAaO4/33XefGjRokCkq07aZl0Zn6mgdE+PLHTQs8bfFFlvomuGR1haIYh+eBxsVoJkDIS3+sPQPAS+keFHFC6n5g+YoAwmQAAmQQGsTSPtB1jw3kj7Iwja7CXipQ4DzQdjuxJ8r4AXQBDu/vW/KwjwJQkL8uUKWspAfSwRhh91li70eZaENrn6WpW1oBwMJkAAJJBHAuy0EnHhftoWdEHQ28r0nqd21njfCTXxAw4dCI8zE88P84Z2biia1kmb+ZiZAAWgTXj3YiYGQ8rPPPtOth+YpXgJga6qaYAtAzz77bL38CloHN9xwQ8UkHMIzaDxceOGFuppnnnlGLyXDMjAECNyMBoWOyPCvnmYDMjSr5ZLi5QrLBvFnAsYThNlGMPrRRx85vcya9EVv8WUWzihshxR4YYUw3ghEISCFGQAGEiABEiCB1iFgz0dq+SBriNg22dLOkWw7ZbC5aUJbKAt9LWM/x4wZo7p3724uRaYt7LoykAAJtBYBKFDgGQEhp9HkhHDTCD1hhqtVA963oRgCISf+sPrP3poVCK3af/aLBGolQAForQTrnB/aEdDmg10qBAgc4SEUy6eqDbY9E9iegid6LMWytQ1QNl4Khg8frpd2nXPOObo6CER32GEHbfwY7TKahlnbAo3Sl156KWs2ps+BAL4ErrPOOvoPxWFSYZwsGKEohO2NXBKCFzKMDzNG8IUTXzaNQBRbLBOk58AcBgSLIAESIIEGEbAFoLV8kDXNt7UZqxGA2i/RbaEscCtjP2HqYL311jOXNdMWmk4TJ07MlIeJSYAEGk9g2rRpegWiEXJCm9PsY4vzrRrwvGrfvr1+1zEmzoyQE+/pZvVlq/af/SKBIglQAOqgC/tQ9gTQkURrYFY7GXOVlyYOnlG32morhS9cCBB+wr5Uz54902SPTBPU2oRwMyj8tDMfd9xxWjsUy+KhPYgv81iOz9AaBPBQha0x/G288ca6U/BKCc1Q2E2DTTQIRrGkpFEBQlq80ODvySef1M2A8BPOJGyhKCYLnCQ06iqxXhIgARLIRiCvD7KmVnt1SdC5kEkT3Nof+2CSxYS2UBb6WsZ+Lr/88uqxxx4zlyLT9pVXXlGPPvpopjxMTAIkUB8C+MhkhJpGg9Ns4TMA8/1WDbCxifcUI9i0NTlpGq5Vrzr7VQYCFIA6rgIcCxkho+O0jsJNq54C0Iceekj17t1b/frrr7p+qL5D+OmyExXV5qj4oBMBeECNC+g7HO3cfvvtOhmWKlMAGkes+c9hOUXnzp19b7joET4SQBBqhKIQjNraO/XuNV5ujcaqqRvL5CEQNU4usI/l9AwkQAIkQAL1IZDlo3LeH2RtUykw45Mm2Ons50VbKAt8ytrPNNeOaUiABMpHAP4jIOS0NTiNkNM2uVG+ltfWImifwwSHLdg0+1jFZptbqa0m5iYBEshCgALQLLQalPaqq65S8PButBcgzHnwwQe1c5s8mmTbR+rQoUOiV1XUCUGSCWY5vjnmtm0QgCB8jTXW0H+mx5jcQAgJYSgEo3BUAe3RRgV8MIDzLvyZgCUltpYonETZWj4mHbckQAL/x955wFtRnP1/8kaTGLvGGgsYYxdUxAJGEHsUK0oTrH8bthhbSFRsMWrsYMESLBgRSaxYsPcCKlawxxI1diUxxrye/3wnmX3n7Nk9dffec879PZ/P3t2dnZ2d/e4598w+8xQREIHGCdQyqZw0IcvvCtZ/WPKx4H3A//BVVlnF7LvvvmUnZENlnndnJ3njlVde6X6nCO+CxwttDR061AwcONCEcT/5nfMStsVv3G9/+1t/KHX98MMPR8fS2sqyX422RWfD+2y0vSzbikBqQwREoKkIYDX/0UcfOeMhr9hE4em3w0mlpup4Bp3BQASX9LiburfqJF6nRAREoLkISAGa8Dz222+/ii7wKH46Qn7zm9+YU045JbpUnz59zI033uhmlKLCBjfC+KF+sFupydBKI3QXq3Sejrc3AWY0Wfr37+9ulM/G66+/XqQU5WW2M11aCN3A8tBDD7k+MjhZfvnlnZUoiZaYYMD9X67z7f1Z1d2JgAg0H4H4hCz/p4cPH+7C7YS95XcFz5ixY8eatddeOzoUn5D98Y9/HB1jQu7nP/+5m0COCu0GMa9nzpxpJk2a5DxtQk+G0A0xbIv6jzzySNhMxe20trLsV6NtcRPhfTbaXpZtVQSsCiIgArkRIDxJ3FXdKzkZU7fzuyDJhr1C01tw+jVZ1vW+kNvHTg2LQC4EpABNwHr88ccnlHZ80ZFHHmlIAuBl8ODBZsKECZmbzK+55pr+Ei6jHhZ8oYVndDDYePXVV6M9rEYlIpBEAOUiCkUWLyjZvYWotxYllmxniVfS8kJ9xx13uG7gluL77ZWiQ4YMMf67goJUIgIiIAIiUJlALZPK4YQsFkXbbrut+fbbb91FmKhab731nDfMk08+ad55551o2/ci/hJO7EgvKC1DjwSsPvmfPmPGDDdRR73JkycbXna9+P/57IdthVaivm6ldVpbWfar0bbi99loeyGzRtuqxFfHRaBVCRBGzIfb6NmzZ6fdBpM/3mrTr72S85NPPum0fuV9YRSYTFAlKTkx6ggt2fPui9oXARHIn4AUoPkzrusKuFaFys9f/vKXhjhaecwyMUDlxQJXMIQM8JUUoCRk8hJaX/gyrUUgjQDWwwzwwkEegc69MhTXeRIudabLDNd+7rnn3OLvg8GRV4aSfAkrVtzplXneE9JaBERABEoJ1DKpHCoJv/rqK9cYcdTOOussM2rUqOj/LYpOxkknnHBCkeVRfEKWiSxCnGC95JWf/N++9dZbi8K3PProo879/eOPP45c4EkGFI5vwrb8XZJ475BDDvG7RWt+R0aPHu0UuEwGprWVZb8abYsbCO+z0faybKsIrnZEoI0I4N3HkrcwbuV/nFdu+jVKTryzqvUCzLufebTP/2D+98eVnH4fV3aJCIhA1yAgBWgTPmdcuMaMGRP17JhjjjGnnnpqtJ/1BkpVXMx8PKuTTz7ZWV2kZYKfMmWKcxejHwsttJDLTJ91n9Re1yJAUi+Wvn37uhtnkIZCHqWoX956663IEqgz6GClyuQAixe+OwyevOs/a1z+KMOVkxd3iQiIgAiIQHUEmJBdZpllnHWnP2P8+PFm77339rtuzcvssccea0iugXLUS6hkpIwJKn5X7rnnHleF8+6//34X89Ofw3rDDTd05ViYeiXAGmusUeRxQ1ubbbaZU576cwcMGGAOPfRQv1u0JlGlt17t1atXSVtZ9iurtriBrJll2bciwNoRAREoIUC+COIke+WmX3v39W+++abknHYpmGeeeVw8Tq/UxE2dcTlr3jE0Jm+XJ637EIHGCEgB2hi/XM4+8MADjf+Bwi0ijAFazwV5efCuCz169HAxsOLtYMFw0UUXuXq4J++4445m2rRpJj4j9tJLL5nDDz88Op2+KoFMhEMbGRFAsditWze3bLHFFq5VLHgIveAVoqyxHO1MQVHLrDlLXHjRRhnqFaKsvaKUiYM8rLnjfdC+CIiACLQSAf4vYmmPezvCCy3jkTTBjT2UjTfeONx121hyevGW/H4/XON+z0uy94ZJCnWy6667FilA11lnnbCJou0zzzwz2o8rcDmQZb+ybKvZ+xZB1YYIdFECTNJ413Sv2ETRyULoEMam7SqECvDxN/3aKzwZW0tEQAREoBIBKUArEcr4uFdEpjU7ceJEZ4Xgjz/44IMuKzvuXixYE/CCgHKFBeVjOKN14oknOjcxfz5rBuEoi5A999wzUQFKBrsLL7zQDBs2zF3ngQcecFZsZC3FjYvjXIsYTiiiEF4WjjvuOLetPyKQNwE+f3zmwhhxWP+EClGU9/XEZsuj73xfsVpliQsTC1g5hUpRrxyNZ0GOn6t9ERABEWhnArzAe8ENvtyELB4rXnbaaaeSSVuOeYUm27S91157mUsvvdSNoShDUBgcfPDBRXWJDcr/ccZaXnbZZReXed6Pg6ZOner2wzq+LSxNEV7Od999d99EtM6yX1m2RQezbC/LtiJ42hCBNieA11FcyclkO2VffPFF294977R4UMWVm+wzTiZGv0QEREAEGiEgBWgj9Ko8l3grXrw7lN+Pr8lCGkqaModBOeJjZPlzGombiGUD1yNhARaoKGu9wpYf3FBWXHFFc8stt8j6M4Si7Q4nwGwvLossiLfIDJWib7zxhkuU0eGdK3NB4qqRdIklLsRITVKOoizVwC9OS/siIALtRIBswky0hsKEbO/evc2gQYNcyB3+f6JcRPk5Z84cV5WJsWuuuaZkTPL5558b4kqHQjJJFAkoJWmXmOZMPt90001hNWeFym/JqquuGpVjkYqLuFeAMg4is3xaW0xYn3feebn2K+t7zLK9LNuKHoI2RKCNCODJhHcdse+9FScu7I28zzU7HowAMKzxSk3W4RJOKDX7vah/IiACrUdACtCcnxkzeCNGjKj6Ki+88ELVdbOuiKUa7u3e/b5c+2+//XbTWNqV66eOdS0CvGyiKGTZZJNN3M3zeUYJGipFGWQ2q4sQrk2+r/Gnh0U2ST68tahfM3DEQlYiAiIgAq1MgORzXvr16+f+F/L/+tlnn3VLktcJiZNQXib9DyRbvP9fjyUmllNM9N55551u8dcK191s+JU333zTFRGTPVSAMk7yE9NYKjGpXa4tklmiuI1Llv3Ksi36mWV7WbYVZ6h9EWg1AkzeoOicNWuWm5hB8cl7YjvKvPPO66zfvXu6XzNeXXTRRRUGqh0fuu5JBFqEgBSgOT4olBhDhgxxP3T+Mpj1lxP/w7juuutGg2xcrs4//3w3W+bPRUkyZswYlyneD+7/+Mc/uuv5On4dt37w5eGaQfzIkSNdQgHKGfBjTYHCZfr06c4NDKuH008/3e1j/YC7/BNPPJH40hG2rW0R6EwCxEZbaaWV3OL7gdUQ309c5vl+sMZSpdmFl3cmSZImSohd5xWirFECM+Bklp3vrkQEREAEmp3A888/H3WRrMh4xeCVgpIx7vGCFdHAgQMNFp3zzTdfdF644WOJUoailIRJxOPEvZ1kIaFgDcoY5+KLL44UoPw2hPLMM89Eu9tuu61TynplaXTgvxtMwjGuSpIs+5VlW/Q1y/aybCuJo8pEoJkJYN2JstMrPHnHSrd/qAAAQABJREFU8x58zdzvavqGwQExlRlneuVmuE77n1xN26ojAiIgAnkS0FtxDnT5cSPu5vHHH1+XC8Po0aMj5efmm29urrvuupJe4ibLQJ1r+QyoJDJC4VqP8DLg41XR9g033BApjFZYYYWoyY022siQGRXXeCwyyHKaZN0QnaANEWhCAgzMSF4RJrDA9dIrQ1GOMlD1bo5NeAslXWKgzRJ3H2WQyoy7V4h65ShrZaovwagCERCBTiQQxkxeZJFFXAxlrELjyk+6iDXVlClTnDXR2WefbRi7xCWMlYfVEa7yjz32mBub8b8SCyxe2n/605+a7t27O6ukyZMnR82E51OIu7wX3N/LhTW699573Xjp8ssvL4m9HrbbaL+ybIt7y7K9LNvy3LUWgWYkEFp3eqVnq1t34oruXdVD5aZ3V48nym3G56I+iYAIiECcgBSgcSIN7qMUJMA+CkUvZBINB/W+PGnNYPq+++6LDhE7qpyQIf6CCy5wg3mUH8QUDBWW5c4Njz311FPR7mabbRYpP6PC/27wAzh48GCXMIki7lMK0Dgl7bcigcUXX9yw/OxnP3PdZ3KB5A3eHR1LIEI/lHvhbcb7xkLcx5WK94/BbagQ5fuNolQuSnFS2hcBEegIAl9++WV0GeKnb7nlls5FFHdzssP37NnTMFnFmIUkeMj48eOdhRVjJyZ8Qgkt+4nf6YV4yuuvv75bfJlfh7GWvbu7PxZagPJbgJtn//793bLaaqu534uHHnrIKWY5h1h+O++8s5uYwhPBS5b9yrIt+pdle1m1RczWq666yuOrae1j2dd0kiqLQAUC7WLdyf+7uHLT7+NZFCbarYBEh0VABESgJQhIAZrhY8ItFdf1MIbmqFGjXFZ2BsbVCJYOfsaQBC+rrLJK2dP44cIiEzd1BOuEehSgoZs88a/KSTiIT3LFLXeujolAqxBAOch3iWWrrbZy3WaGH5c+FIrvvvuuS6TBixlLaOnSKveIkhelLktcfKZ6FKTxhdAYEhEQARHImkCoACV+Jm7qWGei/EJh6YUEIUcddZQLD0QZiZIuu+wys88++/gqbh3+Xw4VoEWVYjuhApRwQ6GEClD+L+Kaj1WpFxIiHXbYYeb222937u8oSegr/cLLxitos+xXlm1xH1m2l1VbTEYec8wxHnNNa5Q4EhFohECrW3cuuOCCqUpO3jUlIiACItCVCEgBmuHTZqDnlZ9YUY0bN85sv/32LgFLtZfB1YDYmyhUqs2CFw7QiXdYjzDb5wWLt3ISZq7GYk4iAl2BAApPb3GE63zfvn2LbhsXTRSJXiEarsPvaNFJTbzDgL9SpnqvGPXu9fwfwSJKIgIiIAL1EAjDjqD8ZEyEyzru8KGgpMRDhokaQg4hKESxtiQunZdwTBSP+enrxNdMDHmJJ1Yi9BD/F/HqOeKII5zbvK8brpk0I3a7D0v04IMPmj//+c9mp512ctWy7FeWbdG5LNvLqi0m+lFy1yM8rz322KOeU3VOFyXgQyJ1VOxO/u8x5kKYqKk1bjtWmoQ08pabfo03D+O0cFKniz5S3bYIiIAIRASkAI1QZLOBuzuz1LjBxwfO1VyBH6uhQ4dWU9XVwb2IH2gvBPGvRzbddNPotHvuuceQiIABZ1w++ugjl5TAlw8YMMBvai0CbU3g17/+tbniiivcPfJCHlojUcigNZ5syQPBqimuHEWhinsk1kGtJih0fWiAeN+xJvCKUdYoR1nzv62e/4nx9rUvAiLQWgTOOOOMIqvCpN7j6k6M8XgcT5SbceVneD5Z4bEORWGB98zjjz8eWexTL0zEUe3/2rAellOhkJSyWiFc0LnnnmseffRRdwoZ0b0CNMt+ZdkWHc2yvazamn/++aPwNNXy9/Wqtfz19bXuWgSawbqTRGre+KRXr15uzBR/Ckz2MCHklZvhmvJqjWbi7WpfBERABLoaASlAM3ziPXr0MK+++mrR7HmGzSc29bvf/S6KSbjMMsu4H8bEihUKsWjbddddXcIlBgNYUVx99dUmVKgyiz5ixAj3okFza621losHWqFpHRaBLk+AlzfCYCSFwmBSIbQWZRvlKC/03qK8lQBiJcvy4osvlnQbCwWvFGXw7pWjDN5rtXgoaVwFIiACTUmABEWEDSknhNVAAcr/ylC22267cLdkm/M23nhjc/3117tjhOXxIUsoCBVw1Vrih3E/Gw33seGGG0YK0DDDfZb9yrKtrJll3Tf3kPVHBBog0NHWnbV2lcliYtGzZpzkFZ1MBPkQGrW2qfoiIAIiIAL/R0AK0P9j0fBWR7t+YvnJi4WXAw44wG/Wtb7yyivdj+ukSZPcTOQGG2xgUOquuOKKLu4h8UW9exrHyACvGce6UOskEYgIoBRk4bsWCsmLsBD1ClHWPvYoStPQTTM8r5m36TcLyeJCYVCPEtQrR1n7RUH4Q1LaFoH2JsD/Qi/LLrusU4jyv++uu+5ysc6Jd87/RaztiZG+7777FiVtjE+8MMnihYmlJPnTn/5k9t9/f/d/mPO5npfQnd6XhWsSLzF2IkkecSpRUtAvPHkGDhzoEjf5+t7Ci/2kflVqK61fWbbV7H3zLLUWgWoININ1Z1o/GfcwzuH/BRM//v/D7rvvbmqxNE9rX+UiIAIiIALJBKQATebS9KUMhLFy8ApJsqP+4he/aKjfuKdee+21zprioIMOcpalBPwPg/5zARI7EdsqPhOJe9cll1xSVx9QikhEQAT+jwDfLywAWLDQDgXlJ/8DvEKUbb+Q8RblaSsJ/X3//ffdQnbnUJhk8crQ+FoWESEpbYtAcxLYb7/9KrrAk0ASCRMKYbF52223meHDh0fJIf0d4pFCoqGxY8cWearEJ4ZCq3vidsaFJEUHHnigYe0FRaaXNddc028WrRmzjBw50vUvPECok5kzZ7pQQSgx1ltvvegw/7+8hP164403DMmTuNdQ4m1Rz0vYryzbov0s28uyLX/vWotAGoFmtu4kvAcJ3VB48s7G2ltIE8pDIgIiIAIi0DEEpADtGM6ZXoU4V8TL8oN0LE+nTJniYhA2ciEG1wzoH3rooagZlDAkOmJQ4ZUqJHfCGnT8+PFFLyuvvfZa3QrQejLXR53Uhgh0MQIoBbGOYokLFg9YOnmFKGv2UZYSM7jVBIUGigCWuDBpE1eKYg2Fy1g8dl/8XO2LgAh0DIHjjz++6guFir2PP/7YbLPNNtHYY/nll3cKRZIZMeH6zjvvuCzxPsYmF4n/T0ThwP8JJoupj/Uo1uYIY6nNN9/clbkC+4d4zbNnz3a7JPBZe+21/SFDfHRc7flfShxoJm28oMyg7zNmzHBJkiifPHly5P7OPnW8hP2iPRYvaW35Sed4v7Jsiz5k2V6WbXk+WosABFrFupPvMws5Ivx3WE9QBERABESg8whIAdp57Ou6MhYMW2+9dRRfjxnFm266qUgRWU/DL730kiERkh+Ed+/e3WVY7devn3PNICbWI488Yg477DB3bbb79+/v3NJ69uzpLrnDDjs4RUs918fy9Je//GU9p+ocERCBgACB8vn+ssSFTPUoAeLKUfarjY8Xb7Mz91FqMHETWkb5/vC/EWWoV4iyRlmKcrSjw5X4PmktAiJQngAWgyg6/QQvE69kOD7rrLOc94mPFczEyG9/+1tzwgknFIUDCRWWXIn6m222mbn11lvdhXGlx6IUd1MytGOtGQpu6D5bPG2F2ZNJjnThhReG1d0EMW17C1YOopDF/R0FLv9vvYTWrfF+UYfJ5nJt+UnoeL+ybIt+ZNlelm3RN0nXJRBadxICDCvwuMV3Z9EpZ93ZWX3SdUVABERABJIJSAGawKWWjKUJp+dWhNUl1hBeSckPLnE4s8jEfvDBB0ftMpC/9957I9cMbgiFAZYS9IEESbfccouL5ceLxHPPPedmNXlRCF28agHh3UBqOUd1RUAEaiNANlwscljiguWTV456i1HWWDmFWZHj5zXrPgpd4vKxxAXrUP5XeaWotyJFOYq1mEQERKBzCGAhteOOO5pzzjkn6gDKz0MPPTTaZwMr+GOPPdY88cQTbjxCGco2xkhxIcGjV4CSVZ7/c2PGjEn8v8ZxL3vvvbffdGsmhLG+9Mnp6MP9999fZNlJRRIfUc5Yyv/vJFwHHjahMOns+8V9o3xdddVVwypRW0w0e2VPUnzALNuiA1m2l2VbRXC007YEZN3Zto9WNyYCIiACnU5ACtCER1BLxtKE03MpItYVg945c+a49kkMgvJz/fXXb/h6uJLdfffdrh0G9xMmTChSfoYXwLrs0ksvdRanWDeQcfWGG25wLyxhPW2LgAi0FgGC8PPyHX8B5y6Id8fEi3en9xakuJN6ZUAr3S2hAFiwfA8FJcSiiy7qrETjylFisaJgkYiACORLIJ54iCRFJHlk/BEK398wZvC3337r3GLjkxiMnYixh1Upk7gsXnBL9bFBmQRCcYkwGUIyklCYCCYppP+/wf8LH4c9rMc2SlZvscl+nz59TDyjfGi5Tt3TTz/dja9QrHqhHKtTr/ykPMlaP8u2uEaW7WXZFn2TtB8BWXe23zPVHYmACIhAsxLQ21yzPpmgXygcGfx7tyystwiW/5Of/CSoVf8mWVW99OrVq6I7PfGztttuO/OHP/zBnfbAAw9IAeoBai0CbUjAZ6oP4/Nxm7yc+0z1KEjD2KMkFQlf2lsBC/eDspcFy/ZQcMPFRdVbi4ZryjkuEQERaJwAk7uhMMbo3bu3GTRokLPwxDoMReXJJ58cTQqjsGTymu9wXLB8J8TOIYccUnQIrxaSR5KACPHWmig2zzvvvERrcJSsXhiTkeTo8MMPd20ss8wy5vnnnzdTp041F198cVFfSCbJ/8NQueknnn17TD4zuYTilftFUTtx4kQX5sjXYc2Y8Ne//nVubXGNZu4b/ZO0LgG+v6+++qqLtYsrOwvxeJtB+O7z2+7jdip2ZzM8FfVBBERABLIlIAVoAs9aMpYmnJ5p0W9+8xtzyimnRG1iRXDjjTcaFBJZCXF0vMQVHL48vu7Ro0dURPIjiQiIQNcjwMuCz1Qfv3te9r3VqLcY9Wusx5MUFfE2mmkfxUdapnosQ+EQKkV97FEsSuEkEQERqEwASzA/KYvFJKF++N49++yzbknKlsy4hVjo3bp1S71AGKPTV5o2bZphicvvf/97p2yNl2M1TuzQUFDm/O53v3NLWO63Ub762Muc6y3sQwt0/n9g3Ypl55133ukWf364xgKVeOwkhMurLa7XzH0LeWi7NQjIurM1npN6KQIiIAJdhYAUoAlPupaMpQmnZ1Z05JFHGgbiXgYPHuzc08Og/P5YI+tQmRpmNS3XZuj2GnfrKneejomACHQNAlg6YRHFEheUBl4ZGq6xIOXlu9UESzBcXlnigjsu1mleIYqS1LvXK1N9nJb2uzqB0PJ6iy22MGPHjjUHHXSQs0iMW4kxubDPPvsYJoqriSOOuztxOO+44w5nXem9ajxzFJEoRPvbBI9JQrggP3GDJw7XJhFT/H8WykwmibEuHTdunEsgSXsvvvhipAAN21phhRUMbv7EHCWLfLxfWIPiHo/VJwko82yLtpu5b+7m9adpCXjrTqw6Z8+eLevOpn1S6pgIiIAIdF0CUoA26bNnUB0qP3HfIjlTHpZEa621VkThwQcfNFg6VXLnxBrDSzzrqi/XWgTaiQCWRwceeKC7pTCbbzvdY0fdCwoCrLWSLLawlkIpijKUJXStx/qp1YQYgW+++aZb4n3Hug3LUSahSJKCQoe1X9gnNmse//fjfdG+CDQDAVzIvaBkJOTO5Zdf7uJgsiapGWMUJhWIgd63b9+qlJ8oJHG7Jc75SSed5NzdyQCPe/1RRx3lLrnQQgulKj+pEE5wdO/e3Z13xBFHuBiiKHwIB7LGGmsYLFJ9vFLc+b3SMkzIFrbF/0F+Ux577LGoX8QZ5R5hwLX4H4ACN++24vfZbH1zD0p/moZAq1h34sq+8sorV/W/ojPg4nm47bbbuksTZ1giAiIgAiKQHwEpQPNjW3fLWAmMGTMmOv+YY44xp556arSf9QYKTAbXWDZ89tln5vrrrzdkTU0TXiKwVvBC3FCJCLQ7Aax0WCT5EsBllPjGSTGOSVLi44z6NcpSlKQ+fl++vcu2dVxeCUEShiGJXwFLWq8Y9etQQeq3UaZKRKDVCfiERNwHn22sEYcMGVLyHeE7TwJGln333dfF/yz3HcB9PBQ8aVCgEr/XK0ArTTR88cUXURN8FxEmi1ESsiQJ9+AlPD/c9m1Rz/crKcFlR7RFH5q5b/RP0jkEZN2ZH3e8QlgkIiACIiAC+ROQAjR/xjVfASsz72K+1VZbFcUArbkxe8L48ePNJ5984k7FCsIH/Pdt8QKAq9ZZZ53likaMGOGsjrbeemtfJVpj4bDbbrtFWUhxUevXr190XBsiIAIikBcBrCF9coL4NfgfFypFvWs9YT38/9P4Oa2wTyxVrGxYygmu9t6S1CtF4wpTyr1lWrm2dEwEOosAkxxeiBW85ZZbugQpKBqx4OrZs6f7LpD9nQlbhDEOFpj33XdfrtbSoas7EzXVSBiyKLRgb9a2uKdm7NsTTzxR1jq33LMIFczl6ulYMQFZdxbz0J4IiIAIiEB7EJACtIOfo1dEpl2WjJ9kN/WCSzov/cSE4kUYK01eBLAK4oWXmFWhnHjiiWbUqFFhkTnzzDOjwP177rlniQKUyrjcExfrhRdeMMzybr/99mb55Zd318IaAOsJYmyR8GjOnDmufV6myQRfyWqiqDPaEQEREIEcCHilXzyRG/8zeZHzClG/Rlnaipnq09Dhas89sZQTfk88q7iC1O/jChxmqy7Xno6JQJYEQgUoYYAY++AGftVVVzmLTX8tLL6x3Dz//PNdEa7sl112mYvL6etkvQ4tI+tRgGLx7aVZ26J/zdg3xp8bbLCBx1fTmv9lYciBmk7uIpWb3boT60gmQPwEKOEg9O7RRT6cuk0REAERyJhAsfYs48bV3H8IYMXghdhV5WTSpElFh0OLAX+ANngp4IU3LvW6gaJMxZVs2LBhzuUMiylc3dOEGDUTJkxwWY/T6qhcBERABDqbAC9JxBFkiccrZlIJC1GvFPVrlIitmKm+GtYomFj+8pe/pFaHGUpQrxBNU5iSAE8voakYdaAOAuG4hnEO31tiY/IZDAXLyvPOO89ZNDPJi6AQ3Xnnnc3CCy8cVs1sm/ihXuhbNcL/GC+Ms7w0a1v0rxn7ttpqq5l77rnH46tpPX36dHPXXXfVdE67V5Z1Z7s/Yd2fCIiACIhAGgEpQNPIZFRO1lJcyqsVLDA7S5hhrZT8yPeNoPxxZYI/prUIiIAItAIBLIPSYm9hEUOcQZSiKERZ+4RM3vW2Fe6xnj5iNctvVzzrdrwt+IVK0nAbhZXfLxebMd6m9tuPAAkcQ6vCpDvE1X2jjTYy8c8Kys248jM8n+R0WIei0OHz+vjjjxtCB+UhYab5aiebw3oLLrhg1K1mbYsONnPfIoDaqJpAK1h3+iRFrGXdWfWjVUUREAEREIE6CEgBWge0ak95+eWXXfB+YlN5IUZbOcHFnIXEQj4O08iRI825557rLHL8uQ899JAZPny4yz5KGXWuuOIKf7hoPXv27KL9tJ1DDz3UvTxwnLiguOMTL47+LLPMMi7u59577+0so6ZNm2YOOOCA1GumXUPlIiACItAKBPjfRxgQlriQqR6lqFeIestR1j5ESPycdtyvJT4pylCvEE2zKFV80nb8lBiXoIjvSjnBmhgFKCEaQtluu+3c5AMWfFjysRCLfKWVVnLusCRA2njjjV3yRs5jErleBWilEEWhYpCM7oQOqiQPP/xwVIV79BK25V3jiWF65ZVXukz3WGjzPUEhNHToUDNw4EATegTl1Rb9a+a+eX5apxNgMoD3Dsb+rEmyF1oip5+Z/xEmOAhp4V3Zmzkze/40dAUREAEREIHOICAFaA7UGWhgtXD88cfXlZl47NixkfKzb9++Ls5m3DKTF4VrrrnGDfxxiWebwXi9WQTJvIpLO8JLKHFIGaAgvFx4wfWdPqGcvfbaaw2WHShLJSIgAiLQVQgQ/4//hSxxwb3cW4yGilG2Q2uw+HntvI9bs2dR7j7D+KShBWmoMFV80nIEW/9YOEm87LLLGj/ZG7dGRqlz++23G8ZLoTfKiy++WBOEWkIUheOrmTNnmkceeaSma4Wu+WFbb775povNfttttxW19/bbbxuuQ2ikXXbZxYWu8BXyaov2m7lv/v61/g8BWXfqkyACIiACIiACtRGQArQ2XhVrP/vss2avvfYyM2bMiOrizoGCsRrB9XDy5MlR1VNOOSXVLR1F5Oqrr26ee+45FxOUhElDhgyJzq1l45xzzokyJdN/r/yMt8H1sBQl2RIDr0svvdSMHj06Xk37IiACItAlCaDE89YtcQAocULlKPFHUcCw4FZfbVzBeLvtsl9LfNI0BalXlio+afN8Kvbbb7+KLvDrrruu6zBjDC8fffSR2XbbbY2PnY419nrrree+J08++aRLbMN3hm0vtVi68X2sJUQRcSi9hNaYvqzSOkzQFraFkpPxlBf+f1CXcSSKXoRxIckoveTVFu03c9/8/XfVtbfu9Baesu7sqp8E3bcIiIAIiEC9BKQArZdcwnm4XjGIJ4GQFzKys4QDSn8sac2Av1+/fgZFKi5jffr0SaoWlRGLEwUowot1vUIGeC9YGpQTlKwoQJGLLrrIHH300coYXA6YjomACIiAJYDVFssaa6xRwoPJL+IkogzFFdev49tY31O3q0oYn5TwLGkSj0+apjCNx5xMa0/l9RPAG6ZaCRV7hJpA8IA566yz3Fhqrrn+M2xF0YnXywknnFDk3ovVaDWSFKKo0nm47pLIKEzUtMIKK5hDDjkk8VQsvpkgRoHL5zG0VA3b8spPvGluvfVWN470DT766KPO/Z3/B17pSpKivNrius3cN8+lK6xl3dkVnrLuUQREQAREoKMJSAGaIXFeXr3yExeicePGme2339688cYbVV9lscUWc7E3OYFBc9z1Pd7Q888/HxUtvfTS0XYtG7xQv/TSS+4U3N+xLC0nq666qqGfH374ocFFixcJyiQiIAIiIAL1ESCbOklSWFCqpAmKHyzXQsVoqCz15V5ZktZOu5fDCWsplnJCNnFvNVouRqnik5ajmN0xJouJOf7OO+9EjY4fP94QfzwUFIrHHnuss5xGOeolVAz6snDN56LeEEUoXzfbbDOnpPRtDhgwwHnF+P1wffPNN0fWq8R157PmhbYYa/nM5txPGHrI19twww1dOZavPlYoEyh5tcV1m7lvnks7rkPrTiw8eXfg89oMotidzfAU1AcREAEREIEsCEgBmgXFoA3c3Y855hjnBo+lQCNSSfn5wAMPRO5RXKt///51XQ73MW9R1K1bN2fhUKkhLARQgCLE3JICtBIxHW91Avfee69T9nMfO+64o2LftvoDbdH+oyghTmIYKzHpVrAe8srQcB1uo0jtqnFJPTPuv5r4pCSGKacgRYmKdS/PR1I/ASYCSIziFaDE2+X/bZrEw/WEMcvj5ySFKGLimOdfrey6665FCtB11lkn9VQUrV7iClzKseT0wmeH+04SwgIw6UxiJIRxZlyybIu2s2wvy7bi992q+7LubNUnl0+/eQ976qmnXOObb7552UnQfHqgVkVABESg6xCQAjTDZ92jRw/z6quvFg0cM2y+qClc1sN4n7hgLbXUUkV1qt3xLxrURwFajTAA9wkAyIYqEYF2J3DFFVcYFmSttdaSArTdH3iL3x8Wi0suuaRbyt0KVmXegjS+RlnKgqK0q8cnnTNnjmHxSqgkpijvSNJUyaJU8UmT6P1fGaGAvOAGjwJ02rRpLkGjL2eN58rJJ58cFe20004ldfzBtBBFeOlsscUWvlrFNSGCyDzv3eCnTp3q9kPFNxPKBx98sLPcpEHGZrvvvntJ2+FniXsm/jpx1ZPaCusSGxTLwLBeeLzRtuholu1l2VYJxBYpaHbrzpVWWskp4JlQUGb2jv9QYS1+0kknuQtfd911UoB2/CPQFUVABLoQASlAM3zYYYD6DJt1TX3wwQcuCD4uMViiPf3009ElRo4caX73u99F+7Vu4LrvBQuXaoQXPC/h+b5MaxEQAREQgeYngGsjS7nYiSh00uKThhalik9acMpiFMaV4pPyG1rOopRjXTE+KYoikgKFgrdL7969zaBBg8w222zjEgbhLo7yE6U0gpXkNddck+rBkhaiiLZrESxScRH3CtBbbrnFZXBHwUkfGZtNnDjR3HTTTa5ZlOLnnXdeSb/4rsyePbvo0hMmTHDWqGlt+cpMWoehh7Jsi2tk2V6Wbfn7b/Y11p0YBvB8WXBn539CMwifR0J0eUUnawwaKJeIgAiIgAiIQFcgIAVoizxlAuFjURCXww47zCUHaGTwwgDVC4P7aiSMP+VjzREPlH7WIwwQJSIgAiIgAs1HgN+XeuOThgpSb1HqlVbNd6cd0yOs9wgh48PIpF01jE9aTmHaTvFJfVJHmJAQEkUfCSFxX2c57rjjSnCROAmFY6WwQ1mEKHrrrbeiZESEKSJW+5133umWko7Zgt///vdOcRs/FoYewkIUBS1jqXJtdbMeOm+++aZrKgw9lGVbNJ5le1m25W68Cf+0gnWnV3jKurMJP0DqkgiIgAiIQIcSkAK0Q3HXfzE/6MXK1Cscae2cc84xuE5ccsklZpNNNqnrAqEFZz0KUB+Y/+GHHzZDhw6tqw/lkn7U1aBOEgEREAER6FACuOQ2Gp/UK0wVn9S4+KzVxidFQdq9e3fzy1/+skOfedYXCxM79unTx0yaNMnst99+TjHos8L7a6L4HThwoMFykhit5SSrEEXPPPNMdJltt93WKWX9+Cw68N8NxmR46CRJGHoIBS6JnIgTint7PNwElqWnn366ufjiiyMFaBh6KMu26GuW7WXZVhLHziy77LLLDBbEzWjd6RWesu7szE+Iri0CIiACItCMBKQAbcanktAn4k4NGzbMxR3kBRFLy8MPP9xZR+Bqt/XWW5vrr7/eMCCvVcIA9fGBd1pbYWZKb3WxwQYbRDES085LK58+fbo5//zz0w6rXAREQAREoE0INBKf1CtIvTUp6/D3qE0Q1XQbPj5pO1iCYmHpBaUu+1iFxpWf1MHVeMqUKS6UwNlnn102ZEBaiKIw9EOlxGJcMww/hPs7FqBpQrgiMrZffvnlzk0+rBdOPBPuABf+xx57zCm9CQFAfFMsQ0k4iWIbK+zJkydHTYTnh9uNtsUFsmwvy7aim2+SDSb/O1P5SYgMYnei7GRhu9JEQJOgUzdEQAREQAREoNMISAGagP6MM84oGgAmVDFbbrml2WijjZIO5VJGzB4vvBQQB2vTTTd1is/77rvPxaM68MADXTD/Wl+CwgFTtRmBw3q4RiK4Z7HUI2F79Zyvc0RABERABNqLQD3xSZMUpCR3ItQLsUzbWRgbtLp8+eWX0S3w3BhroWTC3Rz33Z49expcjsmY/Nlnn7m648ePd3EWGQs1Eg4ounCZjdACFOUnitX+/fu7ZbXVVnOT0g899JBTzNIM8dt33nlnF9cUBZWXtNBDhD1Yf/313eLr+nVS6CGOZdlW1u1l1TcsoYmtWo/wPyEPQelIcq6OED7Xit3ZEaR1DREQAREQgXYnIAVowhPGkoCYU+WELK4dqQBN6guDYSwLsBzAOoIYnLiLjRgxIql6almoAPXu7KmV/3sgdMOHhUQEREAEREAEOppAo/FJ4wrTVo5PivVfq0uoACV+Jl4pWEFeddVVRUpBJk2POuqoyHMEV2RckvfZZ59cEYQK0KWXXtq55jMG8/Lzn//cEJv99ttvd+7vxHmlr/SLxE1eQRtaRjYSeojrZtlW1u1l1TcyyfO865HFFlusntMqnoMCNC+RdWdeZNWuCIiACIhAVycgBWiLfwJwjcJaAMsHhCQBtUpoXfruu+9WdTqz8V4WXnhhv6m1CIiACIiACDQdgXrjk8YVpOyzNKPXQjtYgPrs6nyAUH4uscQSzjU8fm9MAJNdHY+XM888033eUJBhbZnnmIRYnK+//rpzzT/iiCOce3rSh32rrbZyytkhQ4a4ww8++KD585//bHbaaSe3n1XoIRrLsq2s28uqbyiZ/TjXAazhD89rr732quGM6qous8wyLuxCtYYDaa2iFKctLJxZUKwqdmcaLZWLgAiIgAiIQGMEpABN4EfA/XDWOqGKWXfddZOKO6UMtys/MAzjZ1XbGc73Uu35zMZ7IYC/RAREQAREQARanUCj8UnjCtOOjE/arBagtYQVwvItFJSbceVneJys8FiH4haPq/zjjz9uUD7mJcRjr1YGDx5szj33XBeznXPIiO4VoKHnTbXK9LCeDz1Eu1m2lXV7WfUNT6N+/frRvZolLf5rzQ3FTkBxicIyjAsbq5K4K+vORCwqFAEREAEREIEOISAFaALm448/PqG0Y4ruuOMOc+GFFzoLg/XWW89ceumlFS+Mi5WX5Zdf3m9Wvca9jERGWF6QsZOYVVhdpAkuarNnz3aHmd1fe+2106rWXE4mVNz4JSLQbATClxwsf/K0Mmq2e1d/REAE6iOAFSPJeli++eab1HV9rRefddJJJzmryOLSxvawbguVbfW0VktYofnnn7/oEtttt13RfnwHxdjGG2/skkBy7IUXXshVARq/fqX9DTfcMFKAhhnuQ8VgtRaEaaGHsmyL+8myvSzbqsS60nEShfL5yFLwmqrkOUWIAzj4hesTSiEMp5Bln9RW6xHw71T0/LTTTnOTOq13F+qxCIiACFRPgIlrvLM6Q6QA7QzqZa6J9cmNN97oamCNecEFFzgXrzKnuGQA/vg666zjN6tezzXXXGazzTYzt956qzvnrrvuMsOHD089H2tTny0e5WcYmD/1pAoH/BeAxAYsEhFoZgIkuZCIgAiIQDMRYPIyD/G/z3m0HW8zzMROhva4QjRen/0wudCLL76YVKXTyrAQ9PLyyy/7TZfQxu9UUqD5emmhh7IOY5Rle1m25TnUuiaBFvLqq6+6pdbzVV8EOpLAjBkzDItEBERABNqdQKMT7PXykQK0XnI5nde3b98ophDZM3Ht2nvvvVOvRtD/1157zR1nkEc80Hpk1113jRSgWGEOGzYsCtYfb8/H26K8XN/i55Xbp52s2ip3HR0TAREQAREQARHoOAK1hBUKEwpVaxkZus13ZMiBagiG90DSJC9Zhh7Ksi36l2V7Wbbl2dW6xjCgUCjUeprqi4AIiIAIiIAItCEBKUCb7KFiAUqw9rFjx7qekU20T58+ZtVVVy3pKVlPDz/88Kj86KOPTgzIP378eJe0gYo9evQwZCmNC3GtiKVFbE8sMOkD7veh5QcDyIMPPthlMuX8pZZayuy+++7xprQvAiIgAiIgAiIgAo5ALWGFwpjiH3/8scFqMrTwTEKKZZ8XrEbzknvuuce52uO2xXVw7a8ks2bNiqqEWcOzDD2UZVt0Nsv2smwrAqkNERABERABERABEaiTwH/8Quo8WaflQ+D3v/99FFdzzpw5plevXmbMmDHmkUceMW+88Yaz1DzwwANN//79o2RN1DnhhBMSO4TF5q9+9Su3EAMpSYhRRLB+LxMmTHCK0muuuca88sor5rrrrjM77LCDGTdunKtC8HcysBI7VCICIiACIiACIiACjRLAYjCMZf7YY49VbDKMz5xlTPL4hUlCRIz2KVOmmIsuush89dVX8SpF+1ijkv3dS2jd6kMP+WOEHion5UIPZdkWfciyvSzbKsdHx0RABERABERABESgGgJSgFZDqYProFREUbnGGmu4KzPIRrmJe/wKK6xgtt12WzcI9y49WHQSN5SERI3I9ttv7xSdPmPmnXfe6WKBYn1BNtObbropah4l7aBBg6J9bYiACIiACIiACIhAIwSYXA1jkJ988smRB0tSuygjZ86c6Q4ttNBCZptttkmqlkkZWcjx0kFQhl5xxRVl27344ouNj0lKbNORI0cW1Sf0kBdCD/kxnS8L15VCD2XZFtfNsr0s2wqZaFsEREAEREAEREAEaiZgB1ySnAm8/vrrBB9yy2KLLVb11WzG2IJ1sSrYALHR+b4d1t26dStce+21FduzCszo/D333LNifZuptGBjiRbszH10nr9u7969C/fee2/FNlRBBERABERABERABGol8P777xcWWWSRaPxhs7wXvv7665JmrHKxsNxyy0X1Ro8eXVKnUkGt47MRI0ZE17OxRws2k3fiJW6//faisZv1mCmpZ+ODFqy1a9TeHnvsUbAJJovqffvtt4VRo0ZFdWzooYJVvhbVYSfLtrJuL+u+ldy8CkRABERABERABESgSgLfoZ5VbkmamIAdADvXdywJyPKKRSbWofYFIddeY+GAZcVLL73k4n0Sy6l79+6pyZFy7YwaFwEREAEREAER6BIECLtDMkaf1Ij45XidYOH5r3/9y8UixzqUMEEI7uVkTq41LA9hhfCsQewEtSG+Zzn58ssvzbrrrutik1IPi1BiseOJs8wyyxg7gWymTp1qsP70w+tNN93U3HbbbYleOnjvEF7IyxZbbOFiq9vJZoNr/8SJEyPvG6xj4ZLmfZNlW/Qny/aybMuz0loEREAEREAEREAEaiUgBWitxFRfBERABERABERABEQgVwIkehwyZIh57733yl6HxEmE6LFeMWXrJR2sVQFKGy+88IJTWobJl5Lapgzl6Omnn16UUDJed/LkycZ655i///3v8UNF+7jBh4kviw7+dyfLtmgyy/aybCvp3lUmAiIgAiIgAiIgApUIKAZoJUI6LgIiIAIiIAIiIAIi0KEErOu7s4LE4nHhhRcuufaiiy5qjj76aJcgsh7lZ0mDVRZgbYoS9LTTTjM2RFHJWViFYiWK9SZKy+9+97sldcKCXXbZxTz++OPGhh5yCYjCY2xjDWpDD1VUflI3y7aybi/rvtE/iQiIgAiIgAiIgAjUQkAWoLXQUl0REAEREAEREAEREIEOJ2DjdZrp06c7t3gUnmR8/8EPftDh/QgvSIiit956y8yaNcuFKCI8ERapPllSWLea7SxDD2XZFn3Psr0s26qGq+qIgAiIgAiIgAiIAASkANXnQAREQAREQAREQAREQAREQAREQAREQAREQAREoG0JyAW+bR+tbkwEREAEREAEREAEREAEREAEREAEREAEREAEREAKUH0GREAEREAEREAEREAEREAEREAEREAEREAEREAE2paAFKBt+2h1YyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAlKA6jMgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLQtgSkAG3bR6sbEwEREAEREAEREAEREAEREAEREAEREAEREAERkAJUnwEREAEREAEREAEREAEREAEREAEREAEREAEREIG2JSAFaNs+Wt2YCIiACIiACIiACIiACIiACIiACIiACIiACIiAFKD6DIiACIiACIiACIiACIiACIiACIiACIiACIiACLQtASlA2/bR6sZEQAREQAREQAREQAREQAREQAREQAREQAREQASkANVnQAREQAREQAREQAREQAREQAREQAREQAREQAREoG0JSAHato9WNyYCIiACIiACIiACIiACIiACIiACIiACIiACIiAFqD4DIiACIiACIiACIiACIiACIiACIiACIiACIiACbUtACtC2fbS6MREQAREQAREQAREQAREQAREQAREQAREQAREQASlA9RkQAREQAREQAREQAREQAREQAREQAREQAREQARFoWwJSgLbto9WNiYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAISAGqz4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEDbEpACtG0frW5MBERABERABERABERABERABERABERABERABERAClB9BkRABERABERABERABERABERABERABERABERABNqWgBSgbftodWMiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAJSgOozIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0LYEpABt20erGxMBERABERABERABERABERABERABERABERABEZACVJ8BERABERABERABERABERABEWgDAv/7v/9rPvzwQzNnzpw2uBvdggiIgAiIgAhkR2Cu7JpSSyIgAiLQnAT+/ve/mwMOOKCmzn3ve98z8803n1vmn39+s/zyy5v111/fdO/evaZ2WqXyRx99ZA4//PCS7u6///6mT58+JeX1FEyYMMHcc889Racussgi5pxzzikqa+WdjuDYynzUdxEQAREQgWwJPP/88+bmm282U6dONS+99JL59NNPzbfffususuiii5pu3bq5ZaWVVnJjoWWXXTbbDqi1liGgMUrHPar333/fHHXUUSUXHDVqlHufKDmgAhEQgQ4h8J2ClQ65ki4iAiIgAp1EgJcBFG1ZyBJLLGGOPvpoc9BBB5m55547iyaboo0333wzUbl79dVXm+HDh2fSR5TQF110UVFbP/7xj80777xTVNbKOx3BsZX5qO8iIAIiIALZEHj33XfNEUccYa699tqqG/z+979vmNgcPXq0WXzxxas+TxWbnwCWv5dccol5+eWXzVlnnZXYYY1RErHkUvjKK68YJh3iMmnSJLPrrrvGi7UvAiLQQQTkAt9BoHUZERCB9iDwwQcfOEvJ9dZbz3z++eftcVO6CxEQAREQAREQgZYhMHPmTLP66qvXpPzk5r7++mtz7rnnmhVWWMHcd999LXO/6mh5Avfff7/p1auXs/DFylMiAiIgAiKQTEAK0GQuKhUBERCBsgSeeeYZs9NOO5lvvvmmbD0dFAEREAEREAEREIGsCLz11ltm6623bmgSltBAw4YNM0zqSlqXAJ+FwYMHm/79+xuU4hIREAEREIHyBKQALc9HR0VABEQglQDxLP/4xz+mHtcBERABERABERABEciSwJgxY8x7772X2OS8885r1lxzTfPzn//cbLrpps4F93/+J/l1jzYIcePjhSY2qMKmJsAzvu6665q6j+qcCIiACDQTASVBaqanob6IgAh0KAGS+8QT8BAWmZcBln/+85+GeElnnHGGmTVrVmLfiLM0cuTIxGOtVEiM1KSYUbhUSURABERABERABDqfwF//+ldDbO64LLTQQgbF6N577+2SN4bHiQl5zDHHmD//+c9hsdu+++67zaOPPmr69u1bckwFzU/g3//+d0knv/Od75SU+QKN9TwJrUVABLoqASlAu+qT132LgAgYXhh69+5dkcTuu+9uTjnlFHP88ceX1MXlCCuKpZZaquRYKxUssMAC5he/+EUrdVl9FQEREAEREIEuReDxxx9PDL1zxRVXmO222y6RBYlYsBLcZJNNzEMPPVRSRwrQEiRtW6CxXts+Wt2YCIhAlQSSfSKqPFnVREAERKArEPjud79rDjvsMEP21CR5/fXXk4pTy7AuJTtkloHqsVxFEYtClqzqnRWblLhis2fPNmQjbVUhudVLL71knn/+eUOW3X/84x8deivN8iw79KZ1MREQAREQgYoEGDskycYbb5xUHJXNNddc5thjj432w43HHnss3K24nccYJumi/PYynmBhbFFJGPe88cYb5oUXXjCfffZZpeoVj3fkWODTTz91Yw7WrSLwZqz09ttvd+qYL69+YF1LjFXuMe8xNeM+vttffvllZo8/7+8p43zY1Ppdy7tfHmAe36m8PmsdxcSz6eprWYB29U+A7l8ERKAqAsyaDxgwwNx2220l9T/++OOSsngBA/ixY8eap59+2pBAyQ/mf/zjH5uePXuatdZay4waNcosvfTS8VNT97HaOO2008xzzz3nlJ7/+te/orq4QOHqhIXr//t//88MHDjQzD333NHx+Mb777/vEiLEy0ePHm0222yzeHHRPv244IILzFNPPeVCBfBDPt9885n11lvPMTvyyCPN9773vaJzKu08++yzTukcr0fIgh49esSLo/3ddtvN4CIYylZbbWWOOuqosKhom0HS5ZdfbiZMmGBQZicpPHkutDNkyBCz+eabF52fxU6WzzKL/qgNERABERCB5iPwox/9KLFTd955p9l1110Tj/lCEuVstNFGbixArFC/dO/e3VdJXWc5hhk/fnxJ9np+u/faay93/WuuucZcfPHF5sEHHzQohrz069fPeapsv/32vsitZ8yY4UIV3XjjjS50kT+43HLLuTaZwF5wwQV9ceo667HAX/7yF7PnnnsWXY+x5A033ODKPvzwQ3Puuee6kAbU9cLzWGeddcyBBx7oxlC+3K+nTp1qfv/737tdxm5x4bPAeNUL4x/GL0gjYz3Gdpdccom57777nLKWz4RXDKJgZzyLlTFjvtVWW81fPvN1nv0gXATjQSymUX76yXzGsKuuuqqLr0v4LMbV3HMjQpgtxrT333+/YRICRSLxeldeeWU3dj/ggAPMBhtsUNMlsvqeTps2zZx66qlF1yYkFiHB4MJnis+ZV9j/9Kc/NQcffLB7j0mKOZxVv/L6ThXdaLCT52ctKyZBd7VZLQH7wyIRAREQgbYm8MknnzCCLllskoCa7tsqKUvaoF1rKVi2HTtgLPzwhz9MPDfs16KLLlqwA/iybXHQWjgU7GC2Ynth28sss0zBKkpT26bNsL7ftrHGUs+xA8OCDQ1QsIPAxHN9G+uvv37BWggU9t9//5J6dsCc2L4dEJbUpb0HHnggsb4vXGGFFUrOsy8g/nDJ2io9q3o2/l5Y25fMwpw5c0raoqBWjnk8y8SOqVAEREAERKDlCdjJwZLfOH6X5p9//oJVWBSsBVnm95j1GMYqT0ru4de//nXhb3/7W2GLLbYoORb+/rJtw/W4e7TKicJxxx1XsF46Zc9ZZZVVCq+++mpZLlmPBbiYtUQt6ZedmHb9uOmmmwqLL754yfHwXq0iqQAXq2Qs6jvPI6xXadsq9KLzax2j+BOtYrBg48RWdV07AV/YeeedU8dJvs161nn144svvigMHTq0qvuD99prr12wHldlb4G+Jj2bSZMmFawSsWAVionH/TlW6VoYN25c2WuEB7P8nl511VUlfbPGEG4sz/uE72O4tkrvsDvRdpb9yus7FXU22Mjrs8YlsmQSdFmbVRJgZk0iAiIgAm1NIAsFqJ01LzCoC3/s2Ub59/XXXyfys5aEbhAYP6fSvrUELdumtRgt6UelNjluLUdSB2y1DooZkFtLyKr7sdhiixWsRWhJ/c5UgNoZ/oovTmlcBw0alPjMa+HI5yOPZ5nYMRWKgAiIgAi0PAHrlluwlpslv6X+twoFm03MWLBWlAUbZqeh+81jDEOHkhSg++23X8Fmr0+9L39/fm29TgoHHXRQ1fWtRWUBdkmSx1iA66Qpa66//vqaxh7wCqWjFaCXXXZZzRPFPCeU2Wnj4/B+qt3Oqx8YB1gLxqo/S/4ziILypJNOKmAMkCRpClBr2VtgTOzbqbQ+/fTTk5qPyvL4niYpQK11b8Fakaf2G4OIUPLoV17fqbDfbOf1WcuDSbzv2q9MQArQyoxUQwREoMUJNKoAtTFuUgfm1iUrlc4JJ5yQOlCoNOD51a9+ldiudbtJbNO6dxWsa3jBuuaUfTlCCZkktSjuOJ+Xj0r3UM1x61qe1J1C3hagWJr84Ac/aOge+FzEpRaOeT3LeJ+0LwIiIAIi0D4ETjzxxKp+u7AgxAMDK0mb/KjEkrASkTzGMFwzSQGaNMFcbgxRa33aCi0h/b3nNRag/SRlDZPmKM7K3Vv8GNa91j3ad9lZj8XrlNsP77uWMQoXnDVrVsGGT6qpv2FfynngRDdUxUZe/YDr8ssvX/f9ca/nnXde4h2kKUBDPtVsL7nkkmUVyXl8T5MUoOUsrfk+8tkKJY9+5fWdCvud12eNa+TBJOy7tqsjIAVodZxUSwREoIUJpClAf/KTnxRQNIYLA/NDDz20YGPvFGxMqsKGG25YmGeeeRIHRwwGbNzLRDK4tyS5vePmbrO1OjcS+nXLLbcUbIbWkvZp28YKLWobdy8UnfEBk40hVLDxP6O6WGfaTLEFmxShpC7n2vhNUV2/UcugGFehNNctG6+ocPvttxe4NxtPs/CHP/yhwD3H++z3O0sBmvQCyQDu8MMPL9x1110FG6O1YGO7Fh5++OHUcANnn322xxetq+WY57OMOqMNERABERCBtiOAxVk1ruL+d9avF1poIReKJj62SAKUxxjGXydJAer7iLLnt7/9bQFX/w8++KBw5ZVXFmwCytQxBOfBAitOlFncGxPTvr1wTfiauOQ1FuA6ScqasD82TmfBxjot2GSLLkQRypE0xa6NIR91nTEcVr4sSdbAhALyx1mHoYOqHaP4i6V9zhjrTZw40YWAeuKJJ5wlJIra8P7YZizLWLBRyasfgwcPLukz/eY57LLLLk65OWXKlIJNIFZYffXVE+vamPcFuMalkgJ02223LRBmCg8zJtRtPNjUiXksupMkr+9pkgI0/mwxurBxUR0rwiOEkle/8vpOhX3P67OWF5Ow79qujoAUoNVxUi0REIEWJpCmAI3/mNe6j7IsTZIGVTb4feHNN98sOYV4kknuN/vuu29RXQZI8T6iXExzv7HBwgtcEysQ2mcwd/LJJ5coVrlILYNiBoLxfrCPW3iSuxNtp8UM6iwF6I477lhg0Breh034VMTb7/B8kpS4hxxyiK8SravlmOezjDqjDREQAREQgbYkgOUiMffC37BatglhwxghTfIYw/hrpSlAUZbh/REXYpum3Ruu7fFxBxOYSYpBYoHGJa+xANcpp6zZdNNNE8duTMgn3Ssx1JOkW7duJfVReqZJtWMUzv/Tn/5U0jZ9GzFiRKI18d13353o2m+TUKV1p6ryvPrBBHcS64UXXrhw8803l/TNJi0qEKoh6Ry+T3EppwAlnmZSSAZcr5PaT3vfyOt7Wk4BuuWWWxZee+216HYJIcCzDyWvfuX9ncrrswabvJiE3LVdHQEpQKvjpFoiIAItTCBrBSgWoRdeeGEqkffeey9xAPO73/0u9RwC4scHPSgvGXB5sVkiS+pwjs24mRrrC4VrWsIe3y7rWgbFzPrG+4o7+TvvvBM2WbR9/vnnl5xDG52lAKVzWGGSGIEBz5gxY5zValGngx1iH8XvOeklo1qOeT7LoNvaFAEREAERaFMCeH6cdtppicq++O9V0j4eJXg8xCWvMYy/TpoC9JhjjvFVitYoWJL6jyt5UigaTibJZfycpZZaqqhdv5PHWIC205Q1eNBg9ZckKG+ZtI73HUVtkuSpAEXRFe8H418sc9PEW8/h6o+CfsiQIQXGf41IXv3Ya6+9Su6P+0X5V0422mijxPNs5vSi09IUoDz/Dz/8sKiu3+GzmOQ9lmS9nOf3NE0ByvPHO6qc5NmvvL9TeX3W8mRS7lnoWDKBuewXXSICIiACIlAFARsnyNjs68Za/hk7sEs9Y/bs2YnH7IA8sZxCO3ts7KDX2MFPVMe6mhs78DfrrruuK7NZJ41VNBqrFI3qsHHGGWcY645trAuKa8e6vhsb69NYtzFDn7MW685U0uSwYcOMTWhUUu4L7EDT2GymhntqFrEuTsaGQXCLfblI7JZ9wTT33nuvsS8rJcc///zzkrJqC5rlWVbbX9UTAREQARFoLgI2NqOxykRjJ+PMtddea6yrrnnkkUeKxhHlesxvmPXcMHZCzqy88spR1bzGMNEFUja22267xCPWgySx3E7GGmvVmXjMKjtLytPGHx09Fth6663NEkssUdI/Cqzyy43b7GRq0fGvvvqqaB5WKaEAAEAASURBVL8jdpI+B9aTyFgFXurlzzrrLMO4iTEyY9AsJI9+MI622dhLurfWWmuZ4cOHl5SHBYy5bXissMhtz5gxw1jLzpLyeAF1bFLSeLHb57PYrVs38+KLLxYdT3r+SVw4qdF3jaILx3Z22GEHYxOtxUqLdzujX1l9p5L6nsVnPqldqOX5rIqfivZCAlKAhjS0LQIiIAIJBKyVgZk8ebJJG5zHT7GzvvEit2/dXUzaMSowYLexoIrOnTlzZqQApR827pKx8Z+K6rBD29Z1zC3s21laY12sjI0x5JZyyknqVyt21tpYi9KS6uUUwlRmUI+y8emnny45t5kK7My2efTRR42NoRotaS9NNvRA3V1vhmdZd+d1ogiIgAiIQNMQsHEzjXUzdou1NDI33HCDsdZobqzw6aeflu2njZtprHu1m+jzFdPGKY2OYXz7aeu0cQQTv0lis8YnFbsy60FTcqyW3+w8xwJp9+k7bC1z/Wa0trHdo+2O2LBhBYyNWVhyKcZx5cTGySx3uOZjefXDunAba21b0h/GzCghywnjcBSYH330UVE1DBaqkZ49e5atxng5LnHDB453xvfUJlqNd61kvzP6lcV3Kq/PWmc9q5IHo4KIgBSgEQptiIAIdDUC1lXHWUzwo4clBMpGmy3VPP/880UomM3GwmLChAmG2c9K8sorryRWsbGqEsvLFcbbGj9+vLHuN8bG/yp3mmG22CZYcgsVudfTTz/dVBp4lW3UHmTQmCTVWJqihG1UAWqdGZIuH5WFFrRRYYUNmx3X2ADz7mXRZn8kNEyFM/5z2MYrq6peWqXOfpZp/VK5CIiACIhAaxJgItUmcXQLv4c2KZCZOnWqm8S1iYUSb4pJVSzOvBIhPu7wJ2UxhvFtJa1tEp2k4lRLQpvUKbE+hUwC1yodNRawMSbLdi1N4Vv2pIwP2vBAiZbEada4GV8+ai6vftjwUNE1wo1KCl5fl3r1KkDLfW5pP8lyNmls2xnf0+WWW84jSF13Rr+y+E7l9VkDVGcwSX1AOmD+RwxEQAREoKsSsDGKjE2EY2yCG2OzZhrcoKdPn+6sIeJMUJDutNNOxgaojx8q2edHNCuJz1DbZEbGZlmv6IISv/6dd95peHn55S9/GT9U037cQtWfnGRt4Y/5NVYqjUol5WQtFh5YxhAu4Gc/+5mxMV2NjSVWtfKT+yBkQSPS2c+ykb7rXBEQAREQgeYmwG8Uv/u/+c1v3AQvlqFpioIbb7wxupk8xzDRRWIbKP3SflPTLPIYv6VJ2jlJ9Tt6LFBJOZukAEvqd55lcRd8f600JbU/nvU6r35g4Zskyy67bFJxSVlSvTQlV/zkSgwJbVGNdMb3NM11P+xvZ/Qri+9UXp812HQGk/CZaLuYQGNvb8VtaU8EREAEWp4AA89x48YZGwi75F5QvtlERs6SsuRgUGCzjwZ7jW3+4x//KGmA+JFYYp544olmxRVXLDmeVsAMMvGZsGStV5LiatFWfCY8qX1c7RqVSgrOJDehpGu+/fbbzpL2wQcfTDrsYq2iGLVJGQzK46Q4PWkva4kNphR25rNM6ZKKRUAEREAEmpAALraMT1Bo7rPPPmabbbZxCs5qXW+3335795uWdGs2I3xUnPcYJrpQsFGt0ic4xRBKplHpjLFAJWVNLcrbRu8/7fy0OJ9J8dDT2siiPK9+pMVgrWYsy30leWFV4wnFuY16D9EG0hnf03KTDv/pVef0K4vvVF6ftc56Vv55aF1KQC7wpUxUIgIi0MUJoNi6+uqrzRprrGFstssSGqNHjza9e/c2NjN4yTEKsCaNC9YNxOisVdJmW3GhOfbYY93y1FNPmdtuu83ccccdLm4lLvvlBCvQPfbYo1yV1GNpCtdqBo1J8aRSL5RygHAF5eTLL78sd9gdQ5FNSADc3UNhULr77rub3XbbzQW4D93QbBbTsKrbzmoQ21nPsuSGVCACIiACItC0BPjNOuigg0r696c//cmUi4cZnoCny9FHHx0WuW1ih3rpiDGMv1ZnrjtrLJDF5Gne3FZaaaXES4Sfk8QKGRfm1Y+ll146sadpYZ7ilZOSgVb7HYy3Ve9+Z3xPw3FxWr87o19ZfKfy+qzBqTOYpD0flRsjBag+BSIgAiKQQADF46WXXmoGDhxYchQrRLKaY3WRNBvavXv3knOwTCQGZlbJiMIL4OLGQpZ1rvPEE0+YBx54wNx6660uu2tYl+1PPvnEEP+ITJO1CjOkuO/EFY1PPvlk2aYI4F+LC0iacjEpAZO/MBam1ViAkhwirvykjQsuuMDsu+++vrmidVIGziwGXEUXsTsd+Szj19a+CIiACIhA8xIgsSG/O/F4gNddd52zCk373QzvKC2MTWj91NFjmLB/HbndzGOBRjjEPx/1tMXE7GKLLWZIfBkKoYLKCZPxTLJjQEBCJNYsTJ5X8/mMt51XP3Bhx9I2Hlbpsccei3ehZJ/J/KTvUUcrQDvje1rNM+yMfpU8pDoK8vqs0ZVWZVIHxpY4RS7wLfGY1EkREIHOIEA2SNzMkgQFYlo80DQrSRINpAnKwRNOOMFcf/31LhYl2VbjghJx9uzZ5qabbjJnnHGG69tJJ51UVI3ZWeJa4iJHNnPWSZI0e51UL6ksKUg8scWS3PX9+bjdo3itVtLilJWzPiCBQZLEB7hYzMaF0Ad77rlnvDja53nHJd5u/Hi5/WZ5luX6qGMiIAIiIALNQ2CRRRYx6667bkmHSGCER0gl4TfrnHPOSaxGTGoveY1hfPvNsm6GsUCjLJIUUknjx3qus/LKK5ecdvPNN5swXEK8AmNBlKSTJ082Y8aMMYMGDTJrrbVWyaR5/Lxy+3n0A2VXv379Si7L/TF2LickS00a//Xo0aPcaZkf64zvadLnLX5jndGveB/q3c/js0ZfWplJvSyb+jz7BZaIgAiIQFsTsIo30nqXLDauY8X7/uKLLwo2rk/JubRnZ48LVulW0oa1EC2sssoqJefY7IkFm9SopD4FBx54YFF9G9uqYGfNC1ZR6erbGf2C/WEuqkMfrDVIwQ7iE9uk8Nprry05h/NsHKeic2zw78R6NhRAUT12rKVpYt3BgwcX7MC7pL5VWqYytG5IJfUp4JykZ7b++uunXoO2ks6x7v5F17CK65J6PEs+J0lis7WX1Oc6O+ywQ0n1ajjm/SxLOqUCERABERCBtiBgJzUTf4/4DbNK0IL1zki8TxuzsGA9VxLP5ffMZoiPzstjDBM1bjeOOuqokn5Yz5KwSsl20m+7nQAuqecLksYpdoLYH3brPMcCXOCFF14ouU/uw4YsKOpHfKd///4l51nr33g1t5801rST94l1KaxmjOJPPu2000r6Qf+HDBlSsJO4vlq0fv755wswjj8r6jciefXj8ssvL+krfbeKzIKNDZvYZcbUjLvj98g5NvxU0Tkvv/xyST3OmzRpUlG9+E61zz/P7+lVV12V2HdrDBDvbsl+nv3K+zuV12ctTyYlD0AFFQnIAtT+J5KIgAiIQBoB3L0vu+wy5yoTr2P/wzqXaaz5QsFFLck6FLeZzTbbzITWl7hWX3zxxS4LedgGcTxxP/JuE7jqJMX+wtVp1KhRidaVZDYlaVNciEWTFgA+Xjdpn/hhobucr2MHdcYOdF0cUiwQcEcnYz0WK+UsBvz54Zr+JV3j8ccfN/blyXz++eeuOuzvuusuY5WR5q9//WvYROo27lhx4VnSLutQSJJ02GGHhUXRdpJbfHSwzEYzPcsy3dQhERABERCBJiPA71FSBmp+u/AIwdKIWNZsX3jhhc4LZPjw4W4sYRU+iXez9dZbF8UQzWMMk3jhTi7s7LFAFrefFIbp7rvvdp5CVlnkxpeVLBrT+sFnLbQM9vWsEtAQjoHxHS7yjL2swswlD00KQ1RvzHl/vbz6gXVqUlgqOxngrFYvuugiA0PCK+HBRYgkxrjxEANYRfKeUE8iL3+P9ayb9XvarP2qhnFen7VWZlINt5arY38wJSIgAiLQ1gQasQD1YPbff//E2VD7T79w8skn+2rRmtlxq7xMPMcOkgo2+3cBC1SbxTGxDu3GrUtt/MuCVQwm1reD4MIuu+xSOOKIIwr2B7yw0047pbZtM8FH/fQbtVgFcI6Nl5nYD/rNQn+4T7+ftk6zAOUaNhlR6vlzzTVXwSaiKiy44IJFdbCCiV8rbgFqwwiU1PHnbLDBBoVTTjmlcOKJJxa23HLL1HrUx0I3LtVyzPNZxvukfREQAREQgfYh8PDDDxf4DfS/W42sGYM888wzJXDyGMP4izSLBWieYwHuNW9rNa6x8847V/wchJay1Y5RaBuZOnVqxfbLff6wZsT6rVHJqx92krvh79KRRx6ZeHt5W4By0by+p41YgObZr474TuX1WcvrWSV++FRYlgDWLhIREAERaGsCWShAcSuzSYMSB4K4/LzyyislDKdPn16ioCs3UAyPnXnmmSXtUWATLxWsZWhiP8Lz07bTXJFqHRTzQ46yMO068XI7+1kYPXp0SX07+554nxTikleNEtVfa6ONNirY4Psl17CxPUuusd9++5XU8+0krW28qJL6PHdCJIRSC8e8nmXYH22LgAiIgAi0HwEbB7zkNynpt6tcGUpUmywxFU4eYxgu1iwKUPqS11iAtjtCWTNx4sSKnwPu0UstYxR/DpPI5T5HacdsVu3Cxx9/7JtpeJ1XP8aNG1ewVpx13eP2229fsPHvE++tIxSgXDiP72mjCtC8+tUR3yn6ntdnLY9nRX8ltRGQC7z9ry0RAREQgUoEcDPCfQz35bjg8nPAAQfEi02vXr2ci5B3Yy+pkFCAm8R5551nDj/88ISjxmXTvPfee41VxiYeTyukXWtR6TLbp9Wppdy+ODmXIDuwrniaVRQ69yBrlVqxbliBjJrnnntuVZlDrfWrc4VfdNFFwyZSt60VrOnZs2fqcX+A523js7qs8dxHKDx3Av7XK7jfNcOzrLf/Ok8EREAERKBzCFhvD3PjjTe6MUE9PbCxI80dd9xhrCdK6ul5jGFSL9ZJB5phLNDIrRP+Z6mllirbxDvvvFP2eKWDf/jDH8wVV1xhrMdNparRccY3t9xyiyFxV1aSVz8Y4z3wwAMmKcFnWt9JogQTxoDzzDNPWrUOKW/W72mz9quah5LXZ62VmVTDrVXqSAHaKk9K/RQBEeh0AptssolThiV1hDiUdia+5JC1knQxhKxLdWIsJX8CyjWUibNmzTIHH3ywL05cr7766oas8cTcpP1yguJzxx13NMQ0IkaTdXcrV72mY2ROJ0YSg8CkDIfERdpmm23M008/bexsak1t+8oolqdNm2Zs8iNfVLS2LvTGuqw7FvSnWvnhD39onnzySWMDnpukGFpwQ0HKC6K1DnAxU7fYYouS5pOeeUmlMgXN8izLdFGHREAEREAEmpDAdtttZ2bOnOl+g1Fo8rtVTmxyRTNgwAAzduxYNyZgu5LkMYapdM2OPN4sY4F675n+M8ZKGp/QJpPVSfHUa73eyJEjjU1yZKzLvfnRj36UejqfQ2KE8rlMih+aemKVB/LqR58+fVyfGdP27ds3tTfE3x06dKhjQV+aRZr1e9qs/armueX1WWtlJtVwa4U638FgtBU6qj6KgAiIQDsQsDGnDMsHH3xgrNuMS2aANScKxAUWWKCuWyQIvc1YGS0oU2mTxWaeN7z0dIRYVyeXAOm1115zgeU33HDDipYJtfQL5bB1G3cB97EqIJkTAwkUrY0Iz4FnYt2VDPeA4nOttdbKVFlcbf+a5VlW21/VEwEREAERaA4CJE8k4aB1c3bJFvk947cSzwiUYDZudsO/a3mMYZqDnnFjsmYZC9TD5P333zc2nqsbz2CtueSSS7rJ44UXXrie5sqew7UYj8HLj8fwdmokwWbZC6YczKsf7777rhsTkrzUxmw3TFbbTO+ZWrSm3FImxc36PW3WflUDPa/PWiszqYZbM9aRArQZn4r6JAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIikAmB8r4SmVxCjYiACIiACIiACIiACIiACIiACIiACIiACIiACIhA5xCQArRzuOuqIiACIiACIiACIiACIiACIiACIiACIiACIiACHUBACtAOgKxLiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIdA4BKUA7h7uuKgIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0AEEpADtAMi6hAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQOcQkAK0c7jrqiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAh1AQArQDoCsS4iACIiACIiACIiACIiACIiACIiACIiACIiACHQOASlAO4e7rioCIiACIiACIiACIiACIiACIiACIiACIiACItABBKQA7QDIuoQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEDnEJircy6rq4qACIiACIiACIiACIiACDQzga+++spcffXV5qmnnjLPPPOM+eCDD0z37t3NGmusYfbff3+z6qqr1tT9++67z1x55ZXmlVdeMX/5y1/MIossYlZZZRUzdOhQM3DgQPM//yPbjJqAqrIIiIAIiIAIiEDVBL5TsFJ1bVUUAREQAREQAREQAREQARFoewJPPvmkGTlypJk1a1bivX7nO98xgwYNMpdeeqlZYIEFEuv4wo8++si1ddttt/mikvUuu+zilK3f+973So6pQAREQAREQAREQAQaJSAFaKMEdb4IiIAIiIAIiIAIiIAItBEBrD733HNP8+9//9vdFcrOn/70p2attdYyb7zxhpk5c6b517/+5Y717NnToNhcaqmlEglgRTpgwADz2GOPRcex+lxzzTXNjBkzzOuvvx6Vb7nlluZPf/qT+eEPfxiVaUMEREAEREAEREAEsiAgBWgWFNWGCIiACIiACIiACIiACLQBgffff9+5tn/22Wfubrp162auu+4607t37+ju3n77bTNkyBDzyCOPuLJ+/foZ3NuTZN999zWXXHKJO7T44oubW2+91ay77rpR1UcffdS5v3/88ceuDKvTK664IjquDREQAREQAREQARHIgoAUoFlQVBsiIAIiIAIiIAIiIAIi0AYEUGxOmjTJ3cmPf/xj8+yzz7pYnfFbwzoUpSixQZGJEyeaYcOGFVV76623zIorrmi++eYbg2s7lqNYf8blhRdeMH379jWff/65q4eCFWWpRAREQAREQAREQASyIqBI41mRVDsiIAIiIAIiIAIiIAIi0MIEsML0yk9u48wzz0xUfnJsrrnmMueeey6bTk477TS/Ga3POeccp/ykYK+99kpUfnJs9dVXN4ceeiibzrWeuKISERABERABERABEciSgCxAs6SptuomMGXKFBccv+4GypxIni8sDxBiWJFh1K/LnKZDIlBEAEuX//3f/3Vlc889tzLVFtHRjgiIQLsSIObjww8/3K63p/uKEXjwwQfNxhtv7EqJ6fnXv/41VqN0FytR6jG2wn0+tNxEsfniiy+6k+6++24XC7S0hf+UvPTSS2a11VZzO8suu6yLNfrd7343rXpV5bQZuttXdVKVlRgT+HGBPwUGSYs/rrUIxAnwGfKxdjW+jNPRvgiIQLsSYOzw8ssvd/jtzdXhV9QFRSCBABYH//jHP9ygef7550+oUX/RnDlzzJdfflnSAANUBhrf//733cK2RATSCPztb3+LPkdLLrmk+cEPfpBWVeUiIAIi0BYESE6DK7Kk6xDwykru2CsjK939Siut5BSgTDij5Bw6dKg7BXd2FJAI7u+4uJeTVVdd1Sy22GLmww8/dJ87Xowoa0T+/ve/u/HlQgstZBZddNFGmio5l/tj7FqNoMjFYpYlvl3N+arTvgQ++eQT8+mnn7ob/NGPfmTmm2++9r1Z3ZkIiIAIWAJ/+ctfzHvvvdcpLKQA7RTsumgagbPOOssMHz487XBd5b/+9a/Nc889V/FcFK9kMsXahSW0YKh4siq0PYE99tgjSsqAxfL666/f9vesGxQBEejaBBZccMGuDaAL3j0xO710s8mPqhGUNl4Yb3kF6JNPPmlQiiK0xYRzJSHTPApQBGVsowpQf73999/fnHrqqX43k/WvfvUrQ+zSRgTFMNauyyyzTLRgFaOJ1kaotta5xx13nDnppJNcp88//3yzyy67tNYNqLciIAIiUCMBxgQ+8WGNpzZcXQrQhhG2XwMMVq+99lpz//33m1mzZpk333zTLLfccm4Q2qdPHzNixIiWcf/95z//GVkfVHpSWIk+9NBDbqEuA1AUoWuvvbZZY401zA9/+MNKTei4CIiACIiACIiACLQsge7du0d9r9b6F0thLz5zPPvvvPOOL3YK0GinzAbjTZ9Z/pVXXilTs/MPhfdXb2/+9a9/mddee80tYRt4KTER7xWjjEn9NtasEhEQAREQAREQgdoJSAFaO7O2PoOZ+wMOOKAk3hdmysSFGj9+vBk7dqy56KKLTK9evZqexfPPPx/F1am1s++++65hufXWW5270sorr+yUoShFsVAglqhEBERABERABERABNqFQGhxSfb3b7/9tux4BwVeqKj0rrzw+OKLLyIs1bqfL7LIItE54flRYZNsEF4JF/i8BGOEDz74wC0zZswousw888zjDBNCpSjK0SWWWMKFdiqqrB0REAEREAEREIGIgBSgEQpt4PZE4Hs/e0+cot69e5sVV1zRxWl44oknDBaV06dPNxtttJFbE9y+meWZZ57JpHsEKMcVi2XixInOGhR3eaxDUYjiqiQRAREQAREQAREQgVYmQNxPJnhRfJLQ6IILLjAHHXRQ6i2R5T2Ms+7HkJwQKghR2lUjYXxt4nciTMJznXrEt1HPueXOycL6s1z75Y599dVXZvbs2W4J6/HcSFwVV4yiHFVcyZCUtkVABERABLoqASlAu+qTj903A92RI0dGyk8sAK655hqn3PNVX331VRef0ytChw0bZtiuJqaTb6Oj108//XQulyTo/aOPPuoWLoAC1CtDe/ToYeadd95crqtGRUAEREAEREAERCAvAgsvvLAZNWqUIRYhMnr0aLPmmmuafv36lVzy4YcfNieffHJRuc9mTWFowVmPAtQnGCLDfL0KUJIq5SGdqQBNux/G8t57ifF5KMS5J7wAytBQQYqbvTyaQlLaFgEREAERaGcCUoC289Ot4d5wryHmJ0KsyxtuuMGQ1TMULEEpJx4mGQtxjbr55pvNoEGDwmpNs83AORx859kxrCRuu+02tzCQhB2WoSy4zpPxU9LaBCZMmGBYJCIgAiIgAiLQzgRQal5//fUuQyvWnQMGDDAkEdpqq63c+IaYldOmTXNKUjxk+vfvb+677z6HJIxPOffcc0eYQsVoVJiwQXte/AQ7E8xkhK9H8NzZYYcd6jm17DlYuhKn0yd5Klu5CQ7yHEnYFE/axPjUxxb1ytGll17alSn2fcc8uBNPPNGwSERABERABPInIAVo/oxb4gpPPfVU1M/NNtusRPnpD+JaM3jwYHPhhRe6IhSnzaoAZeB25ZVXmjfeeMNgCYo7PAPhb775xt9OLmtm4EkexUIyKawesApFGcognoGlRAREQAREQAREQATyJnDGGWdUnAzecsstXWgj35cFFljAhfthvEdGdsY1uMKzxOW0005zloVeAbrgggtGVUK3a0IoVSNhPd8WbvHEXq9HQjf8es5PO4ex7/bbb29IFIU1KJaXrFnee+89FzIq7dxmKkfhTIgBlrgQt9UrR0OrUcpR/kpEQAREQAREoNUISAHaak8sp/4SS8hLt27d/GbiOrQMjc8kJ57QiYUM0FZYYQW37LzzzoZg/SRGQhmKUjRpwJd1d4nV9Pjjj7uFtnE3Ct3lcUuSiIAIiIAIiIAIiEDWBM4++2ynkCvXLgpPYruHsskmmxgSY+65557OuyU8xjaWnig/9913X7f2x0ML0FAB6t3Zfb20dRizk341s2Dh6seY8X7+7W9/K1GMohwNk0TFz2m2/Y8//tiwzJw5s6hr3/ve98yyyy5b5EqPohQjCW+1W3SCdkRABERABESgSQhIAdokD6Kzu8GgxUslN6PXX3/dV3XKvGinBTYYtK2zzjpuobsMRL0ylAFeRwxMGRTfcccdbkFBS2gBrxBdZZVVDMmnJCIgAiIgAiIgAiLQmQTIKj516lSDyzueQoyXGEcxVhk4cKALmUT/wsnkMCkkVoNesJCsRoj36YV4pK0qTHazMOYMBQtXko56a1FvOUrGdybpW0HoJ58JllAY03LPobWotyANFePhOdoWAREQAREQgY4kIE1LR9Ju4mttuummUe/uueceZyVJrM+4fPTRR2bSpElRMXGhWlkYXGPlwIK8+eabkUIU69a8B6PEjnrllVfcct111xncvODuFaLMsEtEQAREQAREQAREoB4C++23X0UX+HXXXbds0z/5yU8Myy677JJYjySZXvr06eM3DRnlvaD0q0ZCZSrJl9pNGOfhSRV6U3GPjAdxnfeKUdZeOUr8zlYQ7gFFLksYWou+E5bKK0P9GkUpBhia+G+Fp6s+ioAIiEB7EJACtD2eY8N3wQz1rrvualDCofTDXfzqq682vXv3jtrG8nPEiBEGC0aEmJbEh2onwf2fhYD5xAolZqiPH0os0byD3WMZMH36dLfAlThLKENZiCPq42G1E3PdiwiIgAiIgAiIQD4Ejj/++Hwa/m+rxAi9778JkFDu9erVK7oecTtxif7666+dYg/FGFalaYKiz4dkwr2csU9XEawniRHPst566xXd9pw5cxKtRhmPh0mjik5qsh1CIOBhFvcyI3EoStC41Sj7ChHVZA9R3REBERCBNiAgBWgbPMSsboGEQQzAsPBkgLLBBhs4pRsu2sxEowhkEItwjAzw7ZzdnMF3z5493cI9E0gf9y+/EBcpb+Ead911l1t4NsSa8smUVl11VRNmWM27L2pfBERABERABESgvQkw+duvXz837iMeJy7p5eI6Mnb0ySU33HDDorpY9pFY89Zbb3XQGM8MHz48FSCKVJ8tHuUnClWJMcRSxZo2tKiFC8pPbyXq196ClPjzrSAk2KLvLE888URRl1GALrfccs5yNFSQ4maP4lQiAiIgAiIgArUSkAK0VmJtXJ8BLlnLN954Y3PQQQe5rJ9e2Rfe9qhRo8z5559fkgFy8uTJZrfddgurVr29/PLLV123sypifclLAQtC5k9vHUpipTBzaR59xPrUx1yaMmWKi8OFu7xXiLYCwzy4qE0REAEREAEREIFsCDCxSpxPxjgISsttttkmsXEsEy+++OLo2JFHHhlt+w28i7wC9KyzzjLDhg0rGT/6umeeeabfNHvvvXe0rY1kAhghoCBkictnn30WZaj3SlHWhLLK25sp3pd697EIJhxVPOEqinUUoqFSFLd6rGdxtZeIgAiIgAiIQBoBKUDTyHTBcly8R44caR566KHo7n1Ac9xs/IBp3LhxTvE3fvx4s/rqq0d1F1tsMac8jQpq2GAGmFiYrSTE52TZbrvtnMXCSy+9FClEUVR6XnndE6EKiLHk4ywRzxSLCRSiWK62cvKAvJipXREQAREQAREQgfIEfv7zn5v777/fVTr88MNN//79zbzzzlt0EsqprbfeOhq7EfuT/bgQN/S4445ziZIYr+y1117m0ksvLfIgYrx08MEHR9fEJXr33XePN6X9GgiQdIglHkeVsSOK0LjFKPFH857Ir6H7ZatiJUys2DBerD+B0FFxxSjKUcp5p5GIgAiIgAh0bQLfsYOOQtdGoLuHAMo7EiExAEK6d+9uzjvvPGftiAsKblCPPPKIOeyww1xcTOr86Ec/cpYBKNsaFZSpBOon7mg596hGr9NR5/NiQFZ5byFKjKyOlm42lqlXiKKoxqJDIgIiIAIiIALVEsDzgaXaBDbVtqt6zU2AMR9xKImDjqy88sqGWKLE98SC8O6773bhkrxlHhPguK/HXbT9Xd54440utrrf32KLLZyCkzjzjJMmTpxobrrpJncYJRXx6AcNGuSrN7QmrjrXOeaYY8ypp57aUFvtfjLGDknK0U8//bTlbx0vt7hi1FuRanzc8o9XNyACItBiBNBTEOoPnUlHixSgHU28Sa9HjCYGtAjZQO+9914XcyjeXZ8g6ZZbbnGHUKw999xzDc+qtpsCNM6NmXbCCTDQh1dHx2ZicEfMUK8QRcGtmfD4U9K+CIiACIhASEAK0JBG19pG+dm3b1+DK3U5YTL8nnvuKbE0jJ+De/y5554bxQuNH/f7WJFutNFGLub5kCFDfHHdaylA60YXnYhlKJMg3pXeW4++//77FZ9n1EiTbjAWRoHvM9Oz9opSeVI16UNTt0RABFqeQGcqQOUC3/Ifn8Zv4Mknn4yUn8R+mjBhQqLykyuhSMN1CcUnWntm/2+44Qaz4447Nt6RNm7BzzITR4ug9bNmzYoUoq+++qqLt5rn7aO4xiKVBeGl1scOxYIX1yBJeQKXXXaZefjhh12l3/zmN+7lrPwZOioCIiACIiACrUkAa07iwg8ePNglgUy7C1zfq4lBzsS6T5aU1hblt912m1uwEs1CAVruWjpWHQGSUa200kpuCc/AiRDPsSSr0c6w6gn7Vu0294DlK4sPKeXPJZ5okmKUEA3EIc1KeI/yFtAHHnigM0TJqm21IwIiIAIiUEwgu//exe1qr4UIMDvuBfemMK6nLw/XSyyxhIt7+Yc//MEVP/DAA1KAhoAqbBO0HsYsuPvjaoZi0luIfvDBBxVaaPwwGe2J7+VjfBFA3ytE6Zcyr5YyfvDBB80VV1zhDhCuYYUVViitpBIREAEREAERaAMChCTac889o6zs3NKSSy7pQiR98cUX5uWXX3YKTRQ3JM9EcYliKEmI2UiySEl7EcB6ksRDLHEhQRaJtLzVqF+jaMQQoBXkH//4h/uc81kPhQz0fNa9pahXkrJP2LBaBcWrf6fCAhpPPIkIiIAIiEA+BKQAzYdrS7X6+uuvR/2NB0uPDsQ2evToEZWQ8EdSPwESC2BBwYLgUuRjh6IYZQCWt+DaxMKLDLPaobv8T37yE7nL5/0A1L4IiIAIiIAINAkBxiEkJUJxieCqRlxOYml6QbmFhSbx4RmrDB061MUB9cfDNXHmv/76a1fE5OEhhxwSHk7crsaqNPFEFTYFgfnmm8+NJRlPhoLy869//WuJYpQyDAJaQUjcShgAlieeeKKoy3hYxRWjKEgXX3xxg+JUIgIiIAIi0LkEpADtXP5NcXXiN3lh0FuNhG5MCyywQDWnqE6VBLCwYAaYhUEWM89eIcp23jPnvPAQp5TlyiuvdLPZWIf6hVhJEhEQAREQAREQgfYkQMJLH/sTZc6MGTPMIossUnSzyy67rPMiQSmKBwseJddcc40ZNmxYUT12OO5lwIAB5tBDD/W7WncxAnhB8dlhiQvJlrylaOhWTyLRVsnZi4cVi08g5u8R4wIsZb21KGuvKPV1tBYBERABEcifgBSg+TNu+iswW+ll2rRpZsyYMRVnKadMmeJP0TpHAswWr7LKKm7BugJrUBSTXiHKjHneQhwn3L9ZEAZsPpkSFsPzzDNP3l1Q+yIgAiIgAiIgAh1AgPjukyZNiq505plnlig//UGUOiQ26tevnys67bTTKipACbUkEYEkAiQdYol7oxHH3itEGffisYT1JdskaGoFwbjAe1vF+0u5FxSnH330kQmNU/wxrUVABERABBonIAVo4wxbvgWsDL0wkDjhhBP8blVrucBXhSmTSgRkX3/99d1Cg8RS8srQZ5991nRE0Hnv9nPLLbcYZvJR0Pr4oSuuuGJF5XkmINSICIiACIiACIhA5gRCyzXiHJIEqZwQ/xPLNpRRTNAyLsHdNxTGKV4U39CT0LpaAiRgJXRCPPY6VqFYh3rlaGg9ijVpq8hXX30VdfXmm292Ftd4g62xxhrREv9ORSdoQwREQAREoCYCUoDWhKs9K4cWoPXcoTKI10Mtm3MYEG255ZZuYSD4yiuvRMmUyDSft7s87b/wwgtumThxoiGeKVnlvYUoCbMkIiACIiACIiACrUEgVICSCb4aIUM4ClDGIXfffbeLBxqe513g55577hLrvrCetkWgFgL/n70zgb9i6v/4l7JUtEepKNo3SyUlKmWJFluKHtlCdk9IPPZHqUSLyK5SPKksZUmPpULlQUQpSUhJSERa0P2fz3meM/9z5869d+Zuvzv3fr6v1/xm5syZM+e8Z+b8zv3O9/s9mIQJ42Ashx12WNSpUCraClGzjVBfdhivqJPyaAf1xPLaa6/pWsEiFPMvQCmKyUrjTTiWR01gVUiABEggLwlQAZqXtyW3lcLX+JEjR+rFxACFWxMGE/Xr19fB72FZ+PXXXwtc5O1JeTCYvfHGG3NbYV7NkwAGgvgRguWMM87QbkGwxjCzy2Pwl21BAHtMiIAFggGasQ7FwA0WrBQSIAESIAESIIH8JGC742LyIz9iu+ti3IGQPUZQnrHGg/Jmjz320AooTIyEBeMGo9jZc889zWlck0BaBBCeCb9hsNgCr7cNGzbEKEfh3bR582Y7a15twy3+jTfe0AsqZkIFGCtRxBSlkAAJkAAJJCdABWhyRgWfo2HDhoKlZ8+ecs4552jlFWLVYGZD9+yGbhjjxo1zZi93H+N+yRLADwlMTmBmbcXgyShDMWNrLgZ669evFyyvvPKKdo2HchbWoVgwKIULPYUESIAESIAESCA/CNStW9epCGZ69yOrV692spnJk0yCsf7EPmI7jh8/XoYMGaJjOJo8WOPD+2mnnSYYV9oKVTsPt0kgXQKIrY+P81jM+NiUCWMPYymKNZSiWENhmm2PKlMHv2t8VJg/f75ecA68+YwyFOv9999fYBhBIQESIAESiCZABWg0j6LeQ/zGt99+Wx555BG57bbbtOLKDQQKKzMIuOiiiwQLJRwE8IOiS5cueoGbGn6wmPihsMLItksQvrrDLR/L008/ra1BYfVhLETpzhOO54i1JAESIAESKFwCjRs3dhqH2OL43w2lUTzBBDUIv2PEWHuafTv+5+TJk2XSpEnmUNQaH94x+dLcuXPl8ccflxNPPDHqOHdIINsE9t57b8Hzb78DuCZ+9yDEg1GOGsUo9m2vuGzXL1H5mHn+nXfe0QvyoS1GIQqXeXzYoEI0EUEeIwESKBYCVIAWy5322U78czSKTVgMIhYUJjmCawUCc9933326pAMOOEBGjRrls1RmyzcCuM8HHXSQXk4//XTZvn27juNpFKIId5BtwaBx0aJFesG1EMPJxA5FHNG99tor21Vg+SRAAiRAAiRAAhYBxP2EwhOKT4RFeuCBB+Tyyy+3ckRvjh49OmoCxkQWoCgTscI7duyoF1xr5cqV+uP7jBkzdMGwtoMlKDxV4DUCQZo5rhMC/MEkORQSSIcAjD9q166tF3c5UPgbxSjWRjmK5w7GBiUlsGZduHChXlAHhKCyFaKYUIpeWCV1d3hdEiCBkiRABWhJ0s/za8NiELN7Yvnss8/koYcecmoMFyXGc3RwhH4DMbkQ89UEkceAzihD4b7m/kGTjQZj5thXX31VL1DQwkXeKEQx03xJD9TwQ6x9+/a66fiyTiEBEiABEiCBQiOA2IKXXXaZ88Ebcd7hut6hQ4eYpsLi7M4774xKhyWnLbYLPGaLnzNnjp7ExeSBpefVV18ts2fPln79+ulZvbdt2yb9+/eXefPmaas1eKygTqlItWrVUjmN55CALwJ4X0w8TvsEWEYbZahRkGIfC4wObEG80sqVK+skzHifDYHRgR3aDGGy8AHCKEUZliob1FkmCZBAPhLYRX2dKrnPU/lIhHXyJICZxjFohZx66qkpf4n3LFwlPvzww3LxxRcL3KP69u0bLxvTS4AAuoivvvrKiR8Kq2AM7HIpGKjhB5hRiDLYey7p81okQALFSgBx5bDYE+MUK4tiajdihOPDI2J4Q2AROmDAADnhhBO0VSY8gzApJryC4B4Mi064rkPgVTJt2jS9jT/YhgITz9C1116rXXGdg64NuMD36dPHSYXVJ8ac+AhrJld0DvrcgEfLpZdeKoMHD5a77rrL51nMRgLZIYAxNTzsjFLUrDHOhtVmSQkMIfDOG4UoPvpjolsKCZAACWSDQB01yeLGjRtLpN+jBWg27miBlfnss886yk9YfY4ZM6bAWsjmJCIAa0zEDsJyyimnaOUnlKBmQiUM2rL9HQXWIO+9955eUFdYJ5vYoXCXL1++fKIm8BgJkAAJkAAJFCWBu+++O+mkh/jIbTwcAAn/U6dMmSK9e/fWFplwXYcrPBa3DB8+XE+4YhSgUJjb0qtXL3s34TauhzEmXHch+L8PBWjFihVTjgn6/vvvJ7wmD5JALglgTA2rZCz4qG8E4+gvv/xSh6NaunSpXudislJzfVilIuwEFggsUTFBrlGIYjtb1qmmDlyTAAmQQC4IUAGaC8ohvgb+Id90001OCxAflNZ3Do6i3MAACMpHLOeee64g8LrtLv/TTz9lnQu+nr/22mt6wWAS8UyNQhRfsPnVOuu3gBcgARIgARIIAQHEazeWnPGqC4WnrQBFvk6dOsknn3wi5513nrzyyisxp0IpCeUnxoVYG0F6OtK2bVtHAQpFEIUEioEAxrKIy4mle/fu2rAAVtN4B7DA8MA9wVg2ucDTC+8/Fkjp0qW19TcUophUCRNFwTuLQgIkQAJhI0AFaNjuWI7rO3PmTMEM4RC4R1x33XVxa4BYTam6F7ljRsW9CA/kHQFYe8D9DQsEAzajEMWgzR3rSGfK4B8o6VetWqWX6dOn6+cUAzSjEN1///0zeDUWRQIkQAIkQALFQWDfffeVl19+WU+GuXjxYu35gY+g+NAIJY2JBW9PnFi9evW04MDSzAgmSKKQQDESgEIUE85iOemkkzQCuMvbClEYA+RK8DsNSlgsEMTlh/GBsRBFPFHTH+SqTrwOCZAACaRCgArQVKgV0TkjRoxwWnv++ecLAtjHE8zSCau8VATu1ZTCIACFI5aePXsKBkxQoBuFKOKGZdtdHgrXDz74QC8gisDyxmIV63StUwrjLrEVJEACJEACxUAA8dWTudK2atUqIQooOrDEc2fHR0gj7dq1M5sprTFZi5FEY06Th2sSKBYC8MDDgli8EFh2L1u2zFGKYjLRXAli/+IDBRaESjPeWEYhCgtRThiaq7vB65AACQQhwEmQgtAqsryY3dO4RMH1AQNcfInMhnASpGxQzb8y8SMM8YWMQjSXX69BAwO0OirosplMCV+sGdMo/54T1ogESCA/CHASpPy4D/lcix9++EFq1qwpf/zxh3aJxYRF8BiCvPHGGwLPDChmateuLXDHTyaYbOmhhx7S2bA9fvz4ZKckPI4YoK1bt+YkSAkp8WAhEMC7CJd1YyX63XfflVizzHjbKEQx3nbHBy6xyvHCJEACJU4Av8c5CVKJ3wZWwE3giSeecJIQID9byk/nItwoeAKIM3bUUUfpBY2FO4+ZTAmDNkx2lE0xQeYRaB5frKH8xKDMKETRGWPQRiEBEiABEiCBYiQARWaHDh30/+ctW7bIt99+6yg0vXhMmjRJKz9xDPE7jfIT+/ifbhSYiBc4dOhQKVOmDA55CqzK3nrrLecYYg1SSIAE/BHAxErHHHOMXnAGYvLbCtF169b5KygDuezx9qxZs3SJ8A6DQhTvNdaVKlXKwJVYBAmQAAkEI0AX+GC8iiY3XJeff/55p71nnXWWs80NEsgUAePO061bN8EPnxUrVjgK0c8//zzr7vII8g4FLBYI3ONtd3m4z1NIgARIgARIoFgIYBJBfBz85ptvdJMR2sjEIHQz+O233xxrTRxzx4mHIhVl4X8tlKETJ04UWHXGE1h+mhiDVatWlX79+sXLynQSIIEkBDCGxTuIBYJJS22FKN7xbIelsquIOQKwIK4wBJbjtkIU7zyFBEiABLJNgArQbBMOaflz587VZsmofrly5XQ8x5A2hdUOCQEEVMdXYSx9+/YV/LCCu7yxEM1FbCO47uHZxwKB1bOZTAn1si1bdAb+IQESIAESIIECI3DiiScKJraEDBw4UE9yiLGgLb/++qt07dpV8LESgtif2LcF5/Tu3VuefPJJnXzNNddoK9GDDz7Yzqa3X331Vbnxxhud9FtuuUXgNUIhARLIDAG4oCO0mQlvhnfYuMsjlii8o3KpEIVFKha8+xBMoGZc5rHeZ599MtNwlkICJEACFgEqQC0Y3Px/AojZZKRHjx5aCWr2uSaBXBDYa6+95Mgjj9QLrodg7yZ26Mcffyz2RAnZqg9mtsXywgsvCKxiENQdP9wwMQNmx4W7EWMaZYs+yyUBEiABEigJApdddpm21oQ1JiY5admypdx66616jdjdr7/+ukydOlVPwIL64X/hI4884lnV+++/X959911dDv5vH3744VqpCiUrvECggIFFGKw/jfKlc+fOCS1FPS/ERBIggUAEMEkRwlZggSDkhT2p0urVq2Xnzp2BykwnM2KWYjET6sIitHnz5o5StEaNGukUz3NJgARIQBOgApQPgicB888HB+O5PnmeyEQSyBIBDHyw4EcT3OVhdQKFKBb8QMv2IA1x0aB4xWIL3PvwlRo/AN1rpFWpUkVg3UohARIgARIggZIggJjXcD2HQsG4mCeqByw3p02bpj9AwjPis88+k3ihkPA/DgpRxNP2EihZcP2TTz5ZT6YJd/hhw4bpxSs/LE5HjBjB/5tecJhGAlkkgPceHyiwQLZu3ar7C2MlislwMf7OleBjy5tvvqkXXBMxQ6EQNTFEMbEahQRIgASCEqACNCixIsiPWQS/+OILp6UYiMIVqUGDBtKoUSO56KKLtCWck4EbJJBjAlAo4lnEcuaZZ2prUCgmjYUorEUzLcuXL9dWqCi3VatWjmsefsxhMicsXrLrrrtqJWg8BSnSMTkEhQRIgARIgAQyTQBjuksvvVSwDiJQaC5YsECOO+64uP/fUB48NRo2bJiwaCgs4FKPCVqgTI0nbdq0kbvuuovKz3iAmF6QBMaMGSPjxo3TbRs7dmxMKImSajQmLIP1NxbI9u3bBWNhoxCFIQKMA3IlmzZtkvnz5+sF14QHlu0yj0mWOJFpru4Gr0MC4SVABWh4713Wag53JVuMxRtcIWbPnq3/ScM9avjw4YyJaIPidokRKFu2rBxxxBF6QSU2bNjgxA7F84t4oukKBn5wD4IE+QIOy1T88MQSz/IGcc7iKUhhVco4aOnePZ5PAiRAAsVHAAqDY489Vv9PDNp6WH+df/75UcpPeGEgNjYmTzEzSs+cOVMQKglWnvhf7CUo67TTTotSfsL9HeVhlnlTFlzlk5XlVT7TSCDMBDZu3Kito9GGTIxXs8UCcfDNRKG4BgwA8EHDKESxjbRcCSZ1euedd/SCa8La3FiHQjFat25dKkRzdTN4HRIIEQEqQEN0s7JdVcRe+uc//ym33367c6nSpUvLKaecIpgV/r333tMDYWzjayUGtIjZRCGBfCOA+JzHH3+8XqCAhNuOsQ7FTPNBFJi5aNvmzZsFi215bV/XuNl7udgjDTN90s3eJsZtEiABEihuAggN06dPHz2ZYCokrrrqKlm0aJE+Ff9nXnrpJe39YMpauHChdO/eXU+YiUlMLrnkEh031By315ksyy6X2yRAAiVHAGNTuKRjgeD3IfodoxDFeHvbtm05qyAmdUKfZfotfJCxFaIHHnggx8o5uxu8EAnkLwEqQPP33uS8Zo899pgOcm9fGF/in3nmGZ0EpdHQoUO1ghTbDz/8sCBQ/RlnnGGfwm0SyCsCcEFH+AYsmI0WivtPPvnEsRA1lid5VWlXZfy62cdTkCLuG93sXVC5SwIkQAIFSADjs3vuuUeP51JVPqxZs0YmTJig6UDJAfd1hJyxBROnIB0u8LDE+te//iV33313zMzNmSzLvj63SYAE8osAjGYQOgMLfhuiL4IBglGIwn0+FxOYGiq4Fox3sEAwDkbdjNt8/fr1qRA1sLgmgSIiQAVoEd3sRE1FoOnrr78+Jov5qocDsDC7+eabBQHx7733Xp0XFqBUgMZgY0IeE0BMIzvIO559Yx360UcfCb4gh01sN/t4dYcbfTwFKdLhOkQhARIgARIILwGEfIHb+gcffOA0AnHxoIQMIqNHj3Zi+6E8t/LTlAXrKlh33nHHHdr19dFHH5Ubb7zRHNbrTJYVVTB3SIAE8poAfjciPjAWhMDAWBXh1IxCFGGhcunyjw9Cixcv1gvAwaUffZtRiMJQYrfddstrpqwcCZBA+gSoAE2fYUGU8OSTT8pPP/2k2wKXAfOFDv8U3HLNNdfIqFGjBC7zb7/9tlYYUXnipsT9sBCAdSRipGHBMw03dChCoRTF12q49BSCGDd7fI33EgwEEylI4WYPa1oKCZAACZBA/hFYtmyZdlG3JyVBvHYssHoKInBpN9KrVy+z6bmGmz0UoJAHH3xQf0y3Q7JksizPCjCRBEggFAQwhqxXr55eTj75ZD3m/uqrrxyFKPowjFVzJYjtv2TJEidMCJSfUNYahSi2MTamkAAJFBYBKkAL636m3JqnnnrKOff11193JpNxEq2N/fbbT8aPH68DS0N5BKURhQQKgQBmjzSDs9NPP13PeIkv1VCGYrbLQhYMBDGxBRYvwcAV77s9WZN7mwNFL3JMIwESIIHsE4DiwCg/a9asKZjQsmfPnvLll18Gujjc2fHxDwL3d7i4J5LGjRvr/wuY6A//PxADEGmQTJaVqA48RgIkED4CGHNjoiIsiCeM35PoQ4yFKBSimMgtV4L+01wb18SHHFiFGoUo+jWGk8rV3eB1SCB7BKgAzR7b0JT8/fffy/vvv6/rW6VKFWnTpk3Sul988cVJ8zADCYSdABR6LVu21AusnRHQHXLRRRcJJlrCDz68P1iwjZk8EfOoEAWuS6atGJR6SYUKFaIUpG6LUlqKe1FjGgmQAAlkhgDc3QcPHqzd4FP9IIV4eebDdp06dXxZQCGWHv4HQuDWahSgmSwrM4RYCgmQQL4SgEIUfRiWE088UVcTcfqNUhJjT4StypVgPI+PQVimTZumFaIHHXSQoxCFZT28JikkQALhIkAFaLjuV1ZqiwlhjHTq1Elbdpp9rPEPAC7xVF7YVLhdzATwRdjrQwF+NEIJahSibgUp9lOdlCIMvGHtgyWemz2+nNtWo24FKd3sw3CXWUcSIIF8JNCiRQvd96Ybw27t2rVO86AA9SNQWCxYsEBntb0lMlmWn3owDwmQQGERgDU7luOPP1437LvvvnMUolCMYrydK8HvYVi4Y3n22Wf172XMLG8sRKEQ5W/lXN0NXocEUidABWjq7ArmTPwDMYKv+BBMBAM398mTJztxEOGicNhhh8k555yjXRXMOVyTAAn8lwC+XsNNHEs8wbuVSEEKBWKhCpS/idzs4W4EK3S3YtTsQ3kKl0wKCZAACZBANIFy5cpFJ6S4Z8fgQ3/sR/Dxyoh9vr2dblmmfK5JgASKl0D16tUFS5cuXTQEGBYYC1Gs169fnzM4MHrAvAFYXnjhBa0QPeCAAxyFKCaJg2cUhQRIIL8IUAGaX/ejRGpjzw6KQSxclhDUHjP12YI4UlhmzJihXYAxERJN/21C3CaB5ATwdRgL3Gi8ZMeOHY6C1ChKzRoDvUJ2s8fXdbQVSzzBYNIoRN1rKEj59T0eOaaTAAmQQHIC9ke4MmXKJD9B5bDj4m3ZssU5J1NloZx3333XKTfIBiZZoZAACRQmAYz74L2IBYIJfW2FqG2Fnm0CUIiiv8Hy4osv6svVrl3bUYjCUrRSpUrZrgbLJwESSEKACtCElwwsAABAAElEQVQkgIrhMCzSjEC5AjcDBJ3GpCeYAe/ggw/WConFixfLzz//rLM+/PDDOh7i3LlzHZd5xEiZOXOmKSrQ+rPPPguUn5lJINcEunXrJpgADGLW2agDLBxr1aqlF6/yEYsT76lxr7fX2MZSDG72tpulzQk/xN2KUbOPgTKskGCpSyEBEiABEoglYFttpqIARcgkI5kqC3FFjQusKdvvGv0+hQTymcDRRx8tN9xwg65io0aN8rmqeV83GPKAJxYIPp7YClEY/ZgYx7lojPF6euWVV/Tl4M4Py1DjNp/IYywX9eM1SKAYCVABWox33dVmWwE6cuRI+fPPPwWu8E8++WRUnEMoVQYNGiT33XefLmH+/Pny2GOPSf/+/fX+kiVLdPB9V/G+dhFDhUIC+UwAs8JjKWnBhwn8oMOCeENegh+dtmLUtiDFtv2j1Ov8MKehn8IA17Zst9sDN3v3bPa2ghRc6WZvE+M2CZBAMRGwY4hiPOhHYL1vxJ58KVNlIQTTmDFjzCUCrfG/8M477wx0DjOTQC4JwJ3buHTn8rrFcC14DR155JF6QXvxm9coRDGpEjwbc6kQxaROWObMmaPxY0JVowzFGvsUEiCB7BKgAjS7fENR+vbt2516YrCLznfRokVix3RCBlhWjR07VisH7rnnHn0OFKKnnXaaNunv3LmzQCmaiuAfAQeoqZDjOSQQS6B8+fKCJZmbvVGMmrVRmsKFyP5BG3uF8KagXRs2bNBLvFZUrFjRc7ImKEehLN1rr73incp0EiABEkibwN133530QxUsItu3b5/2tdwF2P2bX28CO58d8y5TZSHm35VXXumuqq/9999/n+NLX6SYiQQKnwDCJLVt21YvaC1CdsDC3ChFEc8Tnla5EjMeff311/Ul8YHeVohm0+MsV23kdUgg3whQAZpvd6QE6uOO4wnlplv5aVfrlltu0dahUJrAVR5xmU444QTHKs3O63cb7vMUEiCB3BDw62ZvFKNmbRSkP/74Y0G72SPUB5Z4bvZwCzXKUK81+k+62efmWeZVSKAQCSDGerLJPPCRK9sKUNudPRFnO+4n6mXEVoCmW5Ypk2sSIAESyBQBTB7XunVrvaDMrVu36sl/jUIU48BcGgRgfI3wclggiBlqK0QRU5RCAiSQHgEqQNPjVxBnuycN6dGjR8J2YXCL2CrTp0/X+eBCAAUohQRIoDAI2G728VoEN3qjGDVroyDFvh1aI14ZYU3HANmPm71xrXev8YWfbvZhvfusNwkUNgHEqDMCV00/8u233zrZ7Ek+MlmWcwFukAAJkECWCOAD92GHHaYXXAJekitWrHAsRFeuXCl//PFHlq4eWywMjd566y294Cgs7O0Yoph1nh/cY7kxhQQSEaACNBGdIjlmB2DGlyW3QtQLQ4MGDZxkuA5QSIAEiosAPoRgqVevnmfDMWg0ilGzthWkcLPPpZuRZyWzlJjMzR6DVTObPSZlwjYspdD3Ym22zT7WVJhm6WaxWBLIQwIXX3xxUhf4Vq1aZaXmdmzpeLGU3Rf++uuvnaTmzZs725ksyymUGyRAAiSQIwKIaYzJgLFAduzYIZi411iIYhtpuRJM6rRgwQK94JoYH6KfNVaimFODCtFc3Q1eJ6wEqAAN653LYL3xJcmIXxcl220+l64Bpp5ckwAJ5DcBDBrxQSWeuw6Un3D1MUpRrM02FKY4Zscnzu/WBqsdAu4bN3u/Z2LyJrei1FaQGqWpW4EKawYOhv1SZj4SyA8Ct956a4lVBJNgov9G/7t27VodLznRxByw9ocSAIJJjw499FCn7pksyymUGyRAAiRQQgTwMRofecyHHsydATd5oxBFSDc7JnK2q4n+F6HosEDw+9woRBs1aqTHjRgrwtUf40gKCZCACBWgfAqcThwoNm7cKDDvty08vRCtWrXKSY6n4HAycIMESIAEXATgZg/XcCz2Rxg7G750G6Wo17qQ3extDtjGhyZYzWIJIlB+YvBrlKW2otSkuddGicrBchDSzEsChUGgdOnSekbql156STfotddek759+8ZtHGLVmdniofzEhJlGMlmWKZNrEiABEsgXAujjGjdurJdevXrpsRomUjIKUXhJ+jUuykSbcC1M/IbFLRjTwXMLylCzQGEaZNtdJvdJIIwEqAAN413LcJ3xpQgxRIwLE2aAT6YA/fDDD51a2F/7nURukAAJkECaBGDxiCWemz2+snspRo3LfSG72ftFC2tTxGvFElQwMPZSmJo0t+LU7NNdPyhp5ieB/CJwxhlniFGA3nvvvXLWWWfFtSTHxJlGLrjgArPprDNZllMoN0iABEggDwlAyYjf0FhOPfVUHepp9erVUQrR3377rURqjg/piCmKJVWBdwDGerbSFGNCjBfNmNFLqWrScD69klKlz/MyRYAK0EyRDHE56IjwdX/o0KG6FQMHDtRWoOisvARftZYsWaIPIc9JJ53klY1pJEACJJBVArA0SuRmj8EerNqNQtS9hvI0l7GbsgojC4XDkgALuAURWETAysAoRN3reApUuusHocy8JJA9ArBkuuWWW/SH8cWLF8v5558vjz76aJQLJT6uXHHFFTJv3jxdkRo1asg555wTU6lMlhVTOBNIgARIII8JwNsJH/GxnHzyyYJ+86uvvopSiMLbKSyC0ChYEKYqFYHOAWNAKFCNUjTRtjmGvEbRilArFBJIhwAVoOnQK6Bzr7zySnnwwQe1eyUUBkOGDPHVOsSFwtccCgmQAAnkGwF8iTdu9vHqhoGnWzFq9qEgLSY3+3iMgqbDHTYT7vpuxanZNwpUs8YAme76Qe8S85NAfAL4GDFmzBj9gx25JkyYIJjpHQrO1q1bC7yApkyZIjNnztSF4Eft2LFjPceDmSwrfo15hARIgATynwD6yrp16+qle/fuWiGKWMvGZR7rdCw0850AFMAYV6cztsZ4D+NBoxzFOsg2lKkcM+b7k5Ld+lEBml2+oSkdisxx48ZpN6cglT7ooIOCZGdeEggtASjKTGDzypUr68keQtsYVtwhYNzsMVmHl+CeG4Woew0FKT4YYUBHSZ8AOKbjru9WkJp999ooTrGmu376940lFCaBnj17yjPPPCPnnXeebNmyRebMmaMXr9aOHDlSTj/9dK9DOi2TZcW9CA+QQEgJwCUa7xikYsWKnh8SQto0VjsJAShEjSdT165dde5169ZpheiyZcv0OlVryySXDu1heHcFnUjU3Vh4kHkpTTEu9LJMdafRY8lNNFz7VICG635ltbbNmjXzXT5cnfr37y+HHXaY73OYkQTCTOCqq66SiRMn6iYgTm6bNm3C3BzW3ScBDJL2339/vXidgoGYezZ7oyiFgpRu9l7UMp9m3PU3bNgQqHAoQOMpSN3pZt8MkANdiJlJIIQE4L6OOPGI7fnBBx84kx2ZpsAadMSIEdKxY0eTFHedybLiXoQHSCCEBPAO/fOf/9Q1x0cHvCuU4iVQs2ZNwXL88cdrCN99912UhSjGmJT0CMC4AQuMGFIRKK7dStFElqjIa8aOJh8/wKdCPjPnUAGaGY4FUcpHH33ktKNOnToCize3GX6VKlW04vOmm27SL7JzAjdIgARIoAgJwI0GFvRY4gm+VEMRahSjZg3FKfpZuAIhphIl9wQQAxYD4KCDYMT1MgpR9xqDXHca9rFg4ItzKSSQKwJwt0zHSr1p06aCj374sYj478uXLxd8BIfVPMrGD0G/ksmy/F6T+UiABEggzASqV68uWLp06aKbgfGkcZmHlSjCk1BySwD/U2G1bSy3U7k6fj8YZSjWQRSqJj9i7lOCEyC14MwK9gxbAXr99dfLgAEDBDPXvf/++wIrJyhFMeM7LKIoJEACJEAC/gjApQ1LPDd7lII+Fu7fJjYSXOKwjbW9bdJMvq1bt6al3PDXAuZyE9i5c6dWXkOBHUSgLILrlFGIeilL3WlGmcrA/0FIM2+mCWDsB8+HTHg/ZLKsTLeT5ZEACZBAPhOoVq2adOrUSS+oJ2KuG3d5KEa/+eabfK4+6/Y/Ambcj7F/qgIrUqNE9bIyNUpVszaKU/ucIB8xU61nvp1HBWi+3ZESrA+C2htp1aqV3jzwwAMFC4UESIAESCB7BPAluFKlSnoJchV8hXYrSG0lKY7hCzUGWHY+pGGyIkpuCeB+pequjwkHoRw1ClKjGDXrWrVqyRFHHJHbBvFqJEACJEACJEACJUYA8xIcddRRekEl8GF2zZo12ovTWCli3IFtWPKbMaIZi2BsCC+kdDwFSqzxRX5heDFhcXvs+sViPsobBalZ2wpSbGNCWTxjhSJUgBbKncxAO4wFKKxMmjdvnoESWQQJkAAJkEA2CWDwYqwJg14Hg18MfG3FqBkYGwtTDJjNtsmHwRYl9wTwAwVLPHd9xOSmAjT394VXJAESIAESIIF8IYDJPVP5HY8xnq0wNWNEozzFsUTbHBvmyxPgvx72R/lEk23Vq1ePClD/WJkzLATMlyLUF5MhwdLkjz/+0LGeEO8JnV6LFi30MbrAh+Wusp4kQAIkEJ8AvvRiwZfdIALLUeOuH0SBSnf9IJSD54UinEICJEACJEACJEACQQkYD5Og55n8CA2ED+aJFKWJjkG5CrdwSv4RwG+FQhJagBbS3UyjLcb6E0Xgq9H48eNlyJAhsm7duqhSEWz3tNNOk3HjxknVqlWjjnGHBEiABEig8Ang/wBcrrAEEXxpdluTJlKgmmNYc1CcnDR+vFBIgARIgARIgARIINcEMMEjrE+xpCowvkqmRHVboWKMaNLg4g9FLCWzBOAGX0hCBWgh3c002mLH/5w8ebJMmjTJszRY/kydOlXmzp0rjz/+uJx44olOPgRhXrVqlbMfZGP9+vVBsjMvCZAACZBAyAjAXb98+fJ6CVp1DG4xyMXA2KzNINlLqWryFZNLFi1Agz5VzE8CJEACJEACJJAvBBCGL5UP7Hb9jTLUjBvNfjLrUxzHglBDlGgCtACN5sG9AiFgW4Diywk0/R07dtRLkyZNZOXKlfL222/LjBkzdIs3bNigLUGXLFkiDRo00Glz5syRM888MyUinGgpJWw8iQRIgASKgkCq7vrGmsAoRO01tjHYxdpOh4I1jO76VIAWxavARpIACZAACZAACcQhYMaLcQ4nTYYexChDsY6nQE10rNAmGaUFaNLHhhnCSMBWgO63334CZWbTpk2dpsDS8+qrr5bZs2dLv3795IcfftAzyfXv31/mzZsnsOyB6/ztt9/unBNk44svvpDVq1fLp59+qq1Lg5zLvCSQCwLfffedc5kPPvhAK0icBG6QAAmEhgBc+CtVqqSXeJXGABhKULNgAIxtszbbcLcyaWZdUjOpwgMD3hmZlEIbxGeSDcsKF4Gvv/464+9HuAiwtvlK4KuvvnKqtmzZMqlWrZqzzw0SIIH8IFCqVCnfk44ibBPGiRgjYg2rUvc29t1p5hyk55Nk4/9nSbZxFzVQj+QTYNalZAhMmzZNKyAxGdK1114rdevWjVsRuMD36dPHOQ6r0FNPPdXZT2Xj4YcflosvvjiVU3kOCZAACZAACZBAlgjUrl1bMDagkEAYCbz//vvSunXrMFaddSYBEiABEiCBgiWA2PXwwMq1UAGaa+IFcr127drJwoULdWsGDx4sd911V1otw5eFiRMnplWG18lQzpYpUyYqVqlXPqaRQDICcIvF87T//vtLhw4dBMG+KSSQKoEff/xRXnnlFTn44IOlRYsWqRbD8wqMACxPzYLv07AiwNqkYe21v3btWlm+fLnumw444ICMUmnYsKH07t07o2WyMBLIFYHNmzfL6NGjM345eEThh1uvXr0yXjYLLC4CCNUCQxRMHnPCCScILM0oJJAqAVgRTp8+XerVqydt27ZNtRieVwAEMF60x4zubfe+GWuadCDYuHGjwPMRHxIRFjGTAq9jeBPnWqgAzTXxArneNddcI/fee69uTbdu3WTWrFl52bIaNWoIXi68uBQSSIfAL7/8IhUrVpSzzjpLpkyZkk5RPJcE5N1335UjjjhC7rjjDrn55ptJhATSIoAPiOeee64888wzVMikRZInk4A/Aui/EXYCH7MoJJAuAYwvmzVrpudbSLcsnl/cBBCyC79/L7jgAnn00UeLGwZbnzYBhFbq1KmT3H333dpLOO0C86AAmjDlwU0IYxVgEWIEEyRRSIAESIAESIAESIAESIAESIAESIAESIAESCAfCVABmo93JQR1wmQPRmBhSSEBEiABEiABEiABEiABEiABEiABEiABEiCBfCRQOh8rxTrllsAbb7yhY4V8//33gskORo0albQCK1ascPI0atTI2eYGCZAACZAACZAACZAACZAACZAACZAACZAACeQTASpA8+lulFBdtm3bJuPHj9dX33PPPWXo0KF64qB41cGkDG+99ZZzuGnTps42N0iABEiABEiABEiABEiABEiABEiABEiABEggnwjQBT6f7kYJ1QUzWu++++766lCGJpuN/aGHHpJPP/1U569atar069evhGrOy5IACZAACZAACZAACZAACZAACZAACZAACZBAYgJUgCbmUxRHy5UrJ71793baihnelyxZ4uzbG6+++qrceOONTtItt9wi5cuXd/a5QQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAL5RIAK0Hy6GyVYl/vvv18aNGiga4AJjg4//HC54YYbtKv7l19+KbNmzZJLLrlEunbtKr/88ovO17lzZxkwYEAJ1pqXJgESIAESIAESIAESIAESIAESIAESIAESIIHEBBgDNDGfojm69957y7PPPisnn3yyrFq1Snbs2CHDhg3TixeEgQMHyogRI6RUqVJeh5lGAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAnlBgBageXEb8qMSmMxo2bJlMnz4cKlQoUJMpRAntFWrVjJlyhS55557qPyMIcQEEiABEiABEiABEiABEiABEiABEiABEiCBfCNAC9B8uyMlXB8oOQcNGiTXXnutrFmzRlasWCEbNmyQZs2aSfPmzZ3Jkkq4mrw8CZAACZAACZAACZAACZAACZAACZAACZAACfgiQAWoL0zFl2nXXXeVOnXq6KX4Ws8WkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJFAoBusAXyp1kO0iABEiABEiABEiABEiABEiABEiABEiABEiABGIIUAEag4QJJEACJEACJEACJEACJEACJEACJEACJEACJEAChUKg1G1KCqUxbAcJuAnsu+++0rlzZ2ncuLH7EPdJIBABhIWoVauWdOnSRerWrRvoXGYmATcBxFuuV6+edOzYUWrUqOE+zH0SCESgbNmyOlZ3+/btpVKlSoHOZWYSIIHgBKpWrSodOnSQQw45JPjJPIMEXASqV6+uf680atTIdYS7JBCMQKlSpaR27dr6eUI4OwoJpENgjz32kPr16+vfK9CrFILsElFSCA1hG0iABEiABEiABEiABEiABEiABEiABEiABEiABEjATYAu8G4i3CcBEiABEiABEiABEiABEiABEiABEiABEiABEigYAlSAFsytZENIgARIgARIgARIgARIgARIgARIgARIgARIgATcBEq7E7hPAmEl8Pzzz8unn37qu/oHHnig9OnTx3d+ZixsAs8++6wMGDBAENcryHMEKnPnzpVJkybJ559/Ll9//bVUrlxZEMfpzDPPlO7duwvih1KKi0AqzxP7sOJ6RuK1dvv27TJhwgRZvHix7lO+/PJLqVatmu5T0K+cd955geLGsn+KR5rpJOCPAPtmf5yYy5tAKuMBUxL7b0OCa0MgleeJfZihV9xrji//d/8RA5RCAoVAoGXLlohn63s57rjjCqHZbEMGCHz//fcRFdhZPztK0eC7xB9++CHStWvXhM9cr169Iuofju8ymTH8BFJ9ntiHhf/ep9uCZ555JqImWUvYp+y9996Re++9N/LHH38kvBz7p4R4eJAEfBNg3+wbFTO6CKQ6HmD/7QLJXU0g1eeJfRgfII4v//8ZoAVocX8IKJjW//nnn7J06dKCaQ8bkjsCmzZtkmOPPVY2bNgQ6KJbt27V1p2LFi1yzoN1VvPmzeWDDz6Q1atX6/Rp06bJ5s2bBV9sMVMzpbAJpPo8sQ8r7OfCT+ueeOIJOf/8852sZcqU0TO716tXT7799lv5+OOPBc/Xr7/+KgMHDtT9zOTJk5389gb7J5sGt0kgdQLsm1NnV+xnpjoeYP9d7E+Od/tTfZ7Yh3nzLKZUji+j7zYVoNE8uBdSAsuXLxeYdUPg2n7llVcmbckBBxyQNA8zFDaBlStX6jAIS5YsCdzQq666Sozyc5999pGXXnpJWrVq5ZSzcOFCrSDduHGjvPrqq3LJJZfIxIkTnePcKDwC6TxP7MMK73kI0qIVK1bIFVdc4ZyiLMflvvvuE2WZ7qT9/vvvctttt8nIkSNFfceWKVOmSLdu3TxDubB/crBxgwTSIsC+OS18RXtyOuMB9t9F+9jEbXg6zxP7sLhYi+IAx5cet/n/jUG5RQLhJaDiLzoug/379w9vQ1jznBBQX0Mjw4cPj+y5557Oc6O6R73txwVexfmM7Lbbbjr/7rvvHlGDC896K6vkSIUKFZx8ysrUMx8Tw00g3ecJrWcfFu5nIN3an3LKKU5fpCzSExanrD+dvF79FfunhPh4kAQCEWDfHAhX0WdOdzzA/rvoH6EoAOk+TyiMfVgU0qLb4fgy9pZzZg4PpTCTwkfgo48+ciqt4pw429wgATcBuJG2adNGrr/+etm2bZs+vP/++7uzJdwfPXq0qPh7Og9cVuH67iVNmzYVfMmH7NixQx599FGvbEwLMYFMPE9oPvuwED8EaVZ9586deiI1U8zYsWPNpud6yJAhoj7e6GMqTpwTbsNkZv9kSHBNAukTYN+cPsNiKSET4wH238XytCRvZyaeJ1yFfVhy1oWag+NL7ztLBag3F6aGjMCHH37o1Nh2Q3YSuUECisCyZcu0mzpidBq57LLLZPbs2WbX1xou7UbgqppI+vTp4xx+8MEH5a+//nL2uRFuApl6nkCBfVi4n4V0av/JJ5/o2J4oo2LFinE/qJhrQPnZrFkzsxv17CCR/ZODhhskkDYB9s1pIyyKAjI1HmD/XRSPS9JGZup5woXYhyXFXbAZOL70vrWMAerNhakhI2C+bim3ZD0JTciqz+rmiAAmIzKWmzVr1pT7779fevbsKV9++aXvGvzyyy+CeDoQ5f4uRx55ZMJzGzduLMpNVWCp9c033wji+CCNEn4CmXieDAX2YYZE8a0R5/Opp57SEx2VKlXKFwDEAzWC/3tG2D8ZElyTQGYIsG/ODMdCLyUT4wH234X+lPhvXyaeJ3M19mGGRPGtOb70vudUgHpzYWqICKxZs8axnoFVzB577KGVXFBSYdmyZYu0aNFCW8wYt8EQNY9VzTABuLsPHjxYz7aMZyWovPfee3oCEpxXp04d/bwlK6N+/fpaAYp8n376KRWgyYCF6Hi6zxOayj4sRDc8C1WtXr26nHnmmb5Lxo9kBLU30rp1a7Mp7J8cFNwggbQJsG9OG2FRFZDueID9d1E9Lkkbm+7zhAuwD0uKuaAzcHzpfXupAPXmwtQQETBftlDl5s2by/jx4wUx0tatWxfVitKlS8tpp50m48aNk6pVq0Yd405xEIAifNWqVWJbTAVt+dq1a51ToAD1IxjELFiwQGf9/PPP/ZzCPCEgkInnCc1kHxaCm51HVRw2bJggrhOkVq1aUqNGDad27J8cFNwggbQJsG9OG2HRFJCJ8QD776J5XJI2NBPPEy7CPiwpamawCBTL+JIxQK2bzs1wErBjm0yePFkuvfTSGOUnWqZm0pOpU6dqS9CXX345nI1lrdMiUK5cubSUn7g43FKMVKlSxWwmXFeuXNk5bp/vJHIjlAQy8Tyh4ezDQnn7S6TSsPwcNWqUc+1LLrnE2caG3b+wf4pCwx0SCEyAfXNgZEV7QibGA+y/i/bxiWl4Jp4nFMo+LAYtE+IQKKbxJS1A4zwETA4PAfvrFqxi8E+jY8eOemnSpImOufj222/LjBkzdKM2bNigLUGXLFkiDRo0CE9DWdO8IAD3UyNlypQxmwnXdugFhGSgkIBNgH2YTYPb8Qh8++23csIJJ8j27dt1loYNG8rf//73qOzsn6JwcIcE0iLAvjktfDw5IAH23wGBMXtSAuzDkiJiBkWg2MaXVIDysQ89Abtz32+//WTOnDnStGlTp10nnniiXH311Xqm7379+ulYjNu2bZP+/fvLvHnzZJdddnHycoMEkhGwv9CnogC1Jy9Jdi0eLw4C7MOK4z6n08pNmzbJ8ccfL19//bUuBh/68FHP3Qexf0qHMs8lgWgC7JujeXAvuwTYf2eXbzGWzj6sGO96sDYX4/iSLvDBnhHmzkMCI0aMEMSsgOs7LD1t5addXVjO3HfffU7SW2+9Jc8995yzzw0S8EPAjh+KsAp+5K+//nKypTLxknMyNwqSAPuwgrytGWsUJjFo3769LF26VJdZtmxZmTlzpuf/OvZPGcPOgkhA2DfzIcglAfbfuaRdHNdiH1Yc9znVVhbr+JIWoKk+MTwvbwj06tXLd1169+4tY8aMkYULF+pzMOPiqaee6vt8ZiSBvfbay4EAS2I/YuerUKGCn1OYp4gIsA8ropsdsKmI33XSSSfJ+vXr9ZlQfs6aNUuOOeYYz5LYP3liYSIJpESAfXNK2HhSigTYf6cIjqfFJcA+LC6aoj9QzONLWoAW/eNffADatm3rNNpY1DgJ3CCBJATsAapfd3Y77mf58uWTXIGHSSAxAfZhifkUytHZs2fL0Ucf7Sg/q1WrJm+88UZc5Sfazf6pUO4+2xFGAuybw3jX8qfO7L/z514Ua03YhxXHnS/28SUVoMXxnLOVFgFMHGFk5cqVZpNrEvBFoGbNmk6+devWOduJNhBc2kilSpXMJtckkBIB9mEpYQvVSY8++qh0795dfvvtN13v+vXra8+FNm3aJGwH+6eEeHiQBLJKgH1zVvEWfOHsvwv+Fud9A9mH5f0tSruCHF+KUAGa9mPEAsJGwLbaw6RJFBIIQqBJkyZOdsRO8SNm4hLkbd68uZ9TmIcE4hJgHxYXTUEcuOmmm+TCCy8UE2O4Xbt2smDBAjnooIOSto/9U1JEzEACWSPAvjlraIuiYPbfRXGb87qR7MPy+vakXTmOL/+LkDFA036UWEBJEoA74PTp0+X777+X2rVry6hRo5JWZ8WKFU6eRo0aOdvcIAE/BGCJhYmMtm/fLmvXrpUNGzbIvvvuG/fUX3/9VT777DN9HAHuDz300Lh5eaD4CLAPK757nqjF1113nYwcOdLJgrjVEyZMkD333NNJS7TB/ikRHR4jAf8E2Df7Z8WcmSHA/jszHFnKfwmwD+OTYBPg+PL/adAC9P9ZcCuEBDC5zPjx42XGjBny4IMPytatWxO2ArNxY/Z3I/FmjDfHuSYBN4HSpUtLly5dnOTXXnvN2fbamDt3rmPJBeWnX0WGV1lMKzwC7MMK756m2qKhQ4dGKT+vueYaefrppwP1GeyfUqXP80ggmgD75mge3Ms+Afbf2WdcTFdgH1ZMdztxWzm+jOZDBWg0D+6FjECHDh1k991317VGRz9x4sSELXjooYfk008/1XmqVq0q/fr1S5ifB0nAi8AZZ5zhJN97770SiUScfffGPffc4yRdcMEFzjY3SAAE2IfxOQAB/F+67bbbHBiDBw/WytBddtnFSfO7wf7JLynmI4H4BNg3x2fDI9kjwP47e2yLrWT2YcV2x73by/GlBxf1w51CAqEmcPbZZ0P7pJeyZctGPvroI8/2qBnPIhUqVHDyjh071jMfE4uPwOrVq53nQs20nBSAipETOeCAA5xzzj333IiK1xd13s6dOyOXXXaZk6dGjRoRpaSPysOdwiQQ9HliH1aYz0GQVqkfKk5fccIJJ0SUt0KQ06Pysn+KwsEdEkiZAPvmlNHxxP8RCDoeYP/NRycRgaDPE/uwRDSL4xjHl7H3GZZLFBIINYHNmzdHGjRo4Px4VBahEWU9E5k/f34E/yhmzpwZGTBgQERZ0jh5OnfuHNmxY0eo283KZ45A0AEFrvz88887zxMU8Mcdd1xkypQpkZUrV0amTp0a6dGjh3Mcz960adMyV2GWlNcEgj5P7MPy+nZmvXKTJ092+gr0JXvttVekcuXKvpdx48bF1JH9UwwSJpBAYALsmwMj4wkuAkHHAzid/bcLIncdAkGfJ/ZhDrqi3OD40vu2UwHqzYWpISOwdOnSSL169aJ+RBqrUPd64MCBMdZ6IWsuq5thAkEHFObyzzzzTKRcuXJJnzvlBm9O4boICKTyPLEPK4IHI04Tu3fvnrQPcf8fs/fVpEmeJbN/8sTCRBIIRIB9cyBczOwikMp4AEWw/3aB5K4mkMrzxD6seB8eji+97z1jgKpfEZTwE8BkRsuWLZPhw4eLcnOPaRDihLZq1UqUhZ4gJmOpUqVi8jCBBIIS6NWrl7z77rvSpk0bQfB6t7Ru3VrefPNNUUp39yHuk0AUAfZhUTiKagf/u7Ih7J+yQZVlFhsB9s3Fdsfzo73sv/PjPhRCLdiHFcJdTK0NHF96c9sFelHvQ0wlgXASULEXZc2aNbJixQrZsGGDNGvWTJo3b+5MlhTOVrHW+U4Ak3AtWbJEli9fLirep9SvX1/q1q0rqUxiku9tZf2yS4B9WHb5FmPp7J+K8a6zzZkmwL4500RZnh8C7L/9UGIePwTYh/mhxDxBCISxf6ICNMgdZl4SIAESIAESIAESIAESIAESIAESIAESIAESIIFQEaALfKhuFytLAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQhAAVoEFoMS8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkECoCFABGqrbxcqSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEIUAFaBBazEsCJEACJEACJEACJEACJEACJEACJEACJEACJBAqAlSAhup2sbIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJBCFABGoQW85IACZAACZAACZAACZAACZAACZAACZAACZAACYSKABWgobpdrCwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAQAlSABqHFvCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAqEiUDpUtWVlSYAESIAESIAESKCACSxYsED++usvOeSQQ2Tvvfcu4JayaflMYOPGjbJ27VpdxVq1akmVKlXyubqsmw8Cixcvli1btkijRo2kWrVqPs5gFhIgARIgARIoLAJUgBbW/WRrSIAESIAESIAEfBCYMGGCjB49WuesX7++TJs2zcdZ/82ycOFCueyyy2Tnzp06YY899pDHHntMmjVr5rsMr4wTJ06Uc889V1CfZcuWRWV5+eWX5cYbb3TSHnjgAWnXrp2z72ejbdu2snXrVp116tSp0rBhQz+nhSbPgAEDZNGiRbq+o0aNkk6dOoWm7vlS0eXLlws4zp8/P6pKnTt3ltdeey0qzb3z2WefSe/evd3JKe9ffvnl0r9//5TPz9WJV111lcybN09fbtiwYXLCCSfk6tKBroM+5Oabb5ajjz5a5s6dK7vsskug85mZBEiABEiABMJOgArQsN9B1p8ESIAESIAESCAwgR9++EGWLFmizzOKTD+FQPl5/PHHy6+//qqzlylTRp5//vm0lZ8bNmyQgQMH6jLvvvtu2W233aKqs2nTJqe+OHDeeefJRx99JLi+X/nkk0+0BRjyG0Wo33PDkO+LL75wGP3yyy9hqHJe1RHPWI8ePWTVqlUx9WrQoEFMmjth27ZtDn/3sVT28U6EQb788kun3WCYr3LNNdfIww8/rJXbDz74oFxyySX5WlXWiwRIgARIgASyQoAxQLOClYWSAAmQAAmQAAkUGgG4p9vKz7322kteeeUVOe6449JuKixKf/rpJ+nYsaP07NkzaXkrV66Uf/zjH0nzMQMJ+CVw9dVXO8pPWAfiub700kulb9+++rn3Ww7z5ScBfCwZOnSortz1118v33zzTX5WlLUiARIgARIggSwRoAVolsCyWBIgARIgARIggcIhAOUnXFuN5WeFChW08hNu5enKs88+KzNmzNDFDBkyxHdxY8aMkVNPPVXat2/v+xxmJIF4BOAWbQRWyLAYDCJNmjRJqFRDbNs6deo4RU6ZMkW7YzsJrg28Y2GQNm3ayK67/temBPFS81nOOussueuuu+TTTz/VoQ5eeumlfK4u60YCJEACJEACGSVABWhGcbIwEiABEiABEiCBQiPgVn5WrlxZ5syZIy1btky7qVAKXXfddbqcLl26BIrrCdd9uMLDlb9s2bJp14UFFDcBTHxk5JRTTjGbvtcI25BIAegONYGJeBLl933hEs4YJktsKGoRB/TMM88UxARFXFf0OxQSIAESIAESKAYCdIEvhrvMNpIACZAACZAACaREwK383GeffeTNN9/MiPITFXruuedk9erVum4XXHCBrzpCAWsszhCv0Z4cyVcBzEQCHgQQw9MInnNKYRI47bTTpGLFirpxI0eOLMxGslUkQAIkQAIk4EGAFqAeUJhEAiRAAiRAAiRAAm7l53777actpho3bpwxOEYBgfh83bp181VuvXr1pEOHDgI3ZcjYsWMFSo2jjjrK1/klmen777+Xjz/+WLDed999pXnz5hJP2fbHH3/If/7zH523UaNGgna7J4fy05YdO3bIhx9+KOvXr9eK69q1a/s5LSYPrHUx0znch2vWrKnrjjiwqcqff/6pJ6TZvn27HHrooVK9evVUi3LO++233wQzuWNingMOOEBPzlWuXDnnuN8No2D3m7+k8mWqvab+uCeYXAwfJcAPz102XPHXrFkjmLTru+++kxo1auhnG8+Un5nZ8V7g/uL833//XdcRk1T5fTeQD3GGJ06cKK+++qpgcjS8hxQSIAESIAESKHgCEQoJkAAJkAAJkAAJFBmBESNGRNQgTy/qx39M699+++3I3nvv7eTZf//9I59//nlMvnQS3nrrLad8FcszYVGTJ0928h5++OERNYt7RClnnLSDDjoosmXLloRlKEWYk18pBGPyqliiEaUc0cvrr78ec9xOUPEinbzt2rWzD+nt7t27O8eV4jCiZmWP9OnTJ1K6dGmnDuCP65177rkRpdRxykDd1AQ8Ebu+yKsUpZGnnnrKyefeUK68TtnKsjby448/RlCPPfbYw0lHOUrhFFGTTkWU8shdhOf+woULIyrWa2TPPfeMKkcpCSNK8RR54oknPM+zE1E3tBV1gaAdlSpVcsoDFxXOwD4l0PbMmTMjTZs2dcozzzbqqBTHETXrt2d5Z5xxhnOfzDlYm+cAazVhjue5QRPxHNjXUGEkghbh5E+1vaYA+/n8+eefI+vWrYuoCc4iKpREVB1xzwcMGBBRCktzasxahQtweP3rX/+KOW4SVAgA/azg/bU5mO2GDRtGpk6dGkE+L9mwYYO+F0ohG3M+nh/1YSYybNiwpP0Ayp41a5ZTxjnnnON1OaaRAAmQAAmQQMERoAu8GnVQSIAESIAESIAESMAQeOedd6Rr167OhEcHHnigttSDBWImRSmlnOJ69erlbPvZUIoZmTBhgpQqVUpnhzXY4MGD/ZwaNw8sy8yiRrxx8+EA4jmavFi7BZZ05vgPP/wgnTp1EqUcEqTbgjxox6BBg3QyLD6POeYYHWNVKXTtrNoSFJO4wOI1mSxbtkyUokmUokdgYWkLLEHvv/9+wQRWCCEQT1DXW2+9VU8ypZSgYruI4xwwWLlypY7Divv3008/xStKtxtthTXqokWL5Oyzz5ZNmzY5+XEte985kGRDKXn15Fw9evQQtNktqCPaqJR4eib3tWvXRmWx75N9wNw7rGH5mi+SbntNO+x2g8+RRx6prSFhUWkL7jne09atW+tYu/Yxs22XBd5esnnzZj1hGWL24hn3ElgX9+7dWy/u9w/txmRnw4cPF/UxIeZ01AGWv+gDYAme6FnEyeoDg5QvX16X8/TTTwvqRyEBEiABEiCBQidABWih32G2jwRIgARIgARIwDcBKD/t2d6VVZZWfsIdNtOCCUggysrOt/u7XQfMPm3P1D1u3DiZN2+enSUvtjt27CiLFy/WLu/K2lOU9aM8/vjjWgljKjhq1CgdyxQTskARiHtw3333ybPPPqvbaLuH33bbbWJP2GPKsNc33XSTdmOuUqWKKKs4URa9ms2dd97pKH4weRSUoF4KJZR1ySWXyB133OEoAJW1pFbiwoUfs2djMhnjAj99+nStYIynALPrduGFFzplQrluJrBSlnh2Nl/bF198sVbcmcz9+vWTJ598UvOeNm2aXHHFFaKsA/VhTNwFJZqt5EMbH3nkEb3Ybu94lkz66aefboov8XW67fVqwLHHHitfffWVdkXHc4ltLHARR9gLiLK+1Ep8rFMRhKh4/vnnnVP79u0rU6ZM0e7nb7zxhqBdhj/umzuuL85XFuj6/MMOO0zXDYpUKE1xPu7z7rvvro/jXRszZoxzLa8N5DUhN6CUz8d+w6veTCMBEiABEiCBtAgUnE0rG0QCJEACJEACJEACSQh4ucDD7V0ptBzXUDXAiihFQpKSUju8dOlS5zpwX08mbhd4k19ZqGnXV9QVi1KoRVRcRHM4am27lHu5wCuFqlMnpZyNOte9o5QuTl5lHec+HFEWtM5x1EspkLVLup1RKQsjJ510UlQ+5H3ooYfsbHpbKSuj7o1SHsXksV3gUQ5cwlUsx5h8KoZnpG7dus51lZVnTJ733nsvouIx6jxwL54xY0ZMHiSgrPr16ztlPfbYY575lBLYyYO6NWvWLKIUqTovwhmoGbkjShHleW68RKXodMpU1nwRuP17CZ5ruP3juliUlaBXtoiyJnbyJAun4FlAksR0XeAz2V7384kwGN98801MC+Aaj2OGnVeYArjTm+NeIRqUhbNzHC718dzkJ02a5OTDvVCWyro+ymrXScczHS90g1LIO/nq1KkT15XeNHLIkCFO/quvvtokc00CJEACJEACBUuAFqBqxEIhARIgARIgARIobgKwEITVISZVsQWWWCtWrLCTMrINqy0jqU7Kg/NVTEntQm5c4TF5i4rZaIrOizUmeMJs97DGtAUTvlx11VV2kiiFqFx00UVRadhp0aKFdhs3B9DORIKyldJYlKIzJhsmsYLFqRFs2y7DatSv64U1BG7FKkaryR61RllwITaT1+B5SeZODItfWGmaiWcQzgAhF5DuV+ASDas/I3DVP/nkk81u1Bru3ePHj3fS7rnnnqw8084FsrCR7fbC8rNWrVoxNYcFKCyRjShlpnz77bdm19cabutGYHELN3cvQViEE088UR9C2AGEjICgbzKCe4n3yUuuu+46PaEYJhdDvTFRUiJRcY2dw3Z/5CRygwRIgARIgAQKjAAVoAV2Q9kcEiABEiABEiCBYASUlZdWQBnlJ1zLTXw8xKGEwsId/zHYFWJzw/3aSDoKUJSBWJfXXnutKU4eeOABUZMUOfslvaEml9GznHvVAyEGbFGTE9m7UdvK0tLZT6bcgcvwIYcc4uR3byBm5sEHH6yTobBUk+o4WTBj/IIFC/Q+lE1wp08kLVu21O7RyAMX6dmzZyfKrmM0JqpbwpP/d1BZqIqavEfvQXF36aWXJjwNs36ryap0HsT1hJt1mCSb7VUWnNKqVau4ODp06CBHH320Pg6lOMIf+BW4rSM2JwShDpJ9nIBSG3Fub7/9doGrO8RWzL744ovaPV8fcP1Rk7YJ+jLMLI9QHgivkEjsfkdZpOuYtony8xgJkAAJkAAJhJ0AFaBhv4OsPwmQAAmQAAmQQFoEYP1nlJ9QdiA2p231hZiPAwcOTOsa7pMxMZARWxFh0oKuoTBp0qSJPg1KmvPPP99pU9CyMp0fE8jEk2rVqkUdUuEAovbtHRNvE2nuiY3sfNiOZw1p8sFiExPBGMFkRkbsbSg3YWWbTKCENmJiNZp999pW5LqP+d03SjXkh9UsrEiTiW3Fmg2r5mTXT+d4NtvrZwIyTMxl5PXXXzebSde2ZSUU7rDOTCSwQkds0FtuucVRuh5xxBGiZn7Xp8H6FOXA2hMfOaDMtsXEe7XT4m3bFqCIXZssrm68cphOAiRAAiRAAmEh8N+o6GGpLetJAiRAAiRAAiRAAlkigAl4XnjhBW2phclkYG1lLOXgQty5c2eBZWEmxFaA2oqIVMuGku6JJ57QVn5wn4WFJKzNMNt5SYtbyWnXx7iOm7RELIybv8mbaK1iICY6rI/Zimdb6WkrMMER7unJRMWPdLLY5zuJ1ka9evWsvdQ2bYWgn7biKnY4AEyeEybJZnv98LOfSxWT0zc6213eLsN3ASojnvu77rpLLr/8cm2lCYvlkSNH6gVWn+iX8IzCfd62Fk12jZo1a+rQDSbUA/qkRO9qsvJ4nARIgARIgATynQAVoPl+h1g/EiABEiABEiCBrBOAAgEzjtuWdGoyHu0KDbdSSP/+/bWrbCZmhLcVoLYiLp2Gwgpx0KBBWlmCcqC0hcLWtl5Lp/xUz4WSxq8EiYOZqEw/98jm/sUXXzjF2QpM3Htz/50MSTbs872yJnNN9jrHnZauQtBW+LrLzsf9bLY36LMSZCZ4O6/9vAVljNihaiIr+dvf/iYIy2Hk119/1bPLY4Z5fExo3769jllrYomafF5rzARfvXp1UZMt6cN2n+SVn2kkQAIkQAIkEHYCVICG/Q6y/iRAAiRAAiRAAmkRgAIECgQoBGypVKmSTJw4UY499liBlRRiLp555pkyf/58CeJqapdptm3LR/d1TZ5U1pgMB/Esly1bput8wQUXyCeffCK2+3gq5brPMVZj7nSv/XRZeZWZiTS7DcbFGOXaCiZY7SVzW3bXJZmiKxP3G1a+RuxnyaR5rf/8808nOV/viVNB10Y22+snxIEdciHI/bOtlv3eJ1fTnV2EdYAyfurUqTJ9+nR56623omIT43lGGiZNggs9+oJk17TbkiyvUxFukAAJkAAJkEBICTAGaEhvHKtNAiRAAiRAAiSQGQKY8MhWBNilwr306quvdpIWLlyYdFIcJ3OCDVhzGcEM15kS4wpvFC9fffWVjhcYtHxb4eR1romZ6nUsH9L8MP3666+dqtpu6XaMTljQ/uc//wm0mNm7ncKzsGHXEffYj9j59tlnHz+n5E2ebLbXDl8Qr8Fr1qxxDsFq0q/YMW39XCdZuVDUX3TRRTJnzhxB7OJXXnlFrrrqKmnQoIFzKhShiAn85JNPOmnxNuz3xO6T4uVnOgmQAAmQAAmEmQAVoGG+e6w7CZAACZAACZBA1gkg/l7z5s2d64wYMUIrIJyEFDZsZYOthEihqJhTMOmQPds0XPn9TNxilKYocMeOHTHl2glQvuSzYCbsZGIrQG1Fla1Mwozw+SipKATt9haDAtRve23lZrx7bSsv/cQMNeXYinU/sUO3bt0q//jHP+TRRx8VewIlU569LlOmjGDSpNGjRwtiui5atEjs5wLK0USybdu2KGtnu09KdB6PkQAJkAAJkEBYCVABGtY7x3qTAAmQAAmQAAnkhACsKjEzs3GVhYUVJknyo2SLV0HbiizTClBcE+6vzZo105dHfeEKb7tAe9XLtA/Hkik4YRWZz/LSSy8lrB4sXO08ttLT3v7ggw9k06ZNCcvCQSjFYS16zTXXRJWb9MQUM7gVXckU1rgMYtwaOeqoo8xmKNbZbG+yjwN4fxBWwoif+Jomr60A/eijj5I+SwivMXToULnwwgt1LE+U89hjjwligHbq1EmWLl1qio5Zt2nTJmrSs2TtsvsdhMjIdJiMmAoygQRIgARIgARKmAAVoCV8A3h5EiABEiABEiCB/CcAC1BYghrB5CZnn322npXZpAVZ21Zkq1atCnKqr7xw6ces8CbWI6zh7DiGUOq4xbYKnD17tvuws48Jg6ZNm+bs5+PGM888k1CJC/dgM1kR4nyedNJJTjNgQWssQjHJDCzyEsn333+vXY6hYLz33nsFs3RnW44//nipWrWqvgzu7YMPPpjwklD2InyDkV69epnNUKyz2V4oGBNNdDVjxgwdUxegjNWlX2iNGjVyrMcRNgLWmonk6aefdg6fcsopevvNN9/U93fu3LlRSmwno7Vhf8QoW7asdSR20+53/EwEFVsCU0iABEiABEggXASoAA3X/WJtSYAESIAESIAESogAYoEiJqiR1157TYYNG2Z2A62PO+44J/8777zjbGdyo1WrVlGu8MnKxmRPRqCI8VKCYqZouN3a1mPmnHxaQykJpZmXMhKuwnaIANzDPffc06k+lEi2ogohBOCS7CVwWYbl5++//64P77fffnL66ad7Zc1oGiboGjJkiFMmLH7juTzDivXSSy918uIZPuSQQ5z9MGxks734MHDeeefJL7/8EoNiyZIl8ve//91JHzx4sJQrV87ZT7ax6667Rj1LeK7Qb3jJc889pyddwzF8wDBKarNG+vDhwwWWpF6yc+dOufvuu51DsBhNJHa/g3eFQgIkQAIkQAKFToAK0EK/w2wfCZAACZAACZBARghglmTMCg9ljBEonhYsWGB2fa+bNGkiBx54oM6PyWkSWaD5LtQjI2aDNq7wHoejkjDDfcWKFZ20M844Q896D/f/yZMna7d/lAXLsRYtWkgyCzOnoBLYgELz/fffF7h6jxw5UhDLExZ0N998s3Ts2FGgIIUcccQR0qdPn5gaduvWzbEKhWIJLsndu3fXoRCWLVumlVCPPPKI5oBZtyG77babdlfGOhfSv39/admypb7Uzz//rOsLV2lYoq5cuVJefvllueGGG+TII48UE+cSVqOTJk3KRfUyfo1stvff//63wPL34Ycflo8//ljmzZsnd9xxh7Rr105M7E68s1CABpVjjjlGMIM7BAp5KBsxcdGsWbO0FTIUogMGDIhSnI8dO1aM+zxc7lEPCBTt2B40aJCOEQprbChEMTM87vOLL76o8+E9xr1PJOa5RR482xQSIAESIAESKHgCygWKQgIkQAIkQAIkQAJFRUDFbIQPuF6Ue3ugtiv3audclKFcqCMqZmagMpBZKUGcctTM4QnPVwpIJ+/hhx+eMK/7oFIERpQrvHM+6rx48WJ3Nr3/1FNPRZRiMyqv4WTWStESUXExI0oRrPMpxVFMWV27dnXKUO7yMcdNgrKgdPKhfBWn1ByKWStXZSdv3759Y4536dLFOa6sVyP77ruvs2/qbq979uyp2xFT0P8SlPt7RCmmEpZhygNfZTUbr6iIUro65SjFV9x8QQ8oRW5EWaA6ZZv6eK2bNm0a+fTTT+NeQk2C5ZSzZcuWuPlSPaDirjrlo35qJvPARWWqvfbzqRSrETW7elTd3PxUfM2ICnvhWV+lPHTOxfvjJUrxGTn33HOdfO7y7X30C27BtZWbuq/zGzduHFEKcHcRUfu4F6bNeI//+OOPqOPcIQESIAESIIFCJEALUDXioJAACZAACZAACZCAXwJwScUkSEZgXYdJhoJKjx49nFPiuS87GdLYgJWgX8s1WIFigiNYRtqzwuPymCUaVniwlrMtRdOoWtZOPfjggwUWbl7tqFmzpo7V+fzzzydsByaFGT9+vG4vXMZNPFW70kiD+/SKFSs8LUntvNnYrlatmkyfPl2U4k3PAA4rZbfA0hjxa3FflXLMfThU+9loLywy3333XWnbtq3nM48QCYjDacfIDQpt77331jF5YZ3bsGFD8bpPhx56qGASJDv8grkOrg3LVIQ9MLFfzTGzRlzhB+w0owAAQABJREFU888/P2Y2eHPcXsNq3bj8w8LU69m283ObBEiABEiABAqBwC7Q6hZCQ9gGEiABEiABEiABEggTAQzB4FKurPK0O/n69eulfPnyedMEuNvCvRYKXntioLypoM+KYPIZxP3EhEbKelagAE1FMNP6Z599pifEQZxQKBYxWVI+zZ6tLDd1/VDPypUrC5RicN32UrilwiDfzkm1vVD6mY8OmNDLxG3FM4LJopSFs1Yo4/1EPM5MC+r9ySef6HAStWvX1oppvwpWZa2p30lMfoXQGXieoTy1Q3Mkqy+U9hMmTNDZoAyF8pdCAiRAAiRAAoVOgArQQr/DbB8JkAAJkAAJkEDeEkB8zb/97W+6fpjJ++KLL87burJiJFAoBOIpQAulfYnagTiksObGBw5MiBVvUqZEZfAYCZAACZAACYSRAF3gw3jXWGcSIAESIAESIIGCIIAJeMxkJyrGZUG0iY0gARLIXwIq3rBWfqKGN910U/5WlDUjARIgARIggQwToAI0w0BZHAmQAAmQAAmQAAn4JYA4m2a25vfeey+lGeX9Xov5SIAEipvAzp075b777tMQ2rdvL2pyruIGwtaTAAmQAAkUFQEqQIvqdrOxJEACJEACJEAC+UZAzQ4tamZ1Xa1BgwblW/VYHxIggQIhMGnSJFm6dKmOa3r//fcXSKvYDBIgARIgARLwR4AKUH+cmIsESIAESIAESIAEskJg11131ROSlC1bVt555x3B7OQUEiABEsgkgW3btsnNN9+si8S6RYsWmSyeZZEACZAACZBA3hOgAjTvbxErSAIkQAIkQAIkUOgEEAd02LBhuplwif/zzz8LvclsHwmUGIGqVatKrVq19FKmTJkSq0cuLzxmzBhZu3attGzZUgYPHpzLS/NaJEACJEACJJAXBDgLfF7cBlaCBEiABEiABEig2AlEIhGZPXu2IE4f4vNVqFCh2JGw/SRAAhkiMH/+fPn111/l4IMP1orfDBXLYkiABEiABEggNASoAA3NrWJFSYAESIAESIAESIAESIAESIAESIAESIAESIAEghKgC3xQYsxPAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQGgJUgIbmVrGiJEACJEACJEACJEACJEACJEACJEACJEACJEACQQlQARqUGPOTAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmEhgAVoKG5VawoCZAACZAACZAACZAACZAACZAACZAACZAACZBAUAJUgAYlxvwkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKhIUAFaGhuFStKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQlAAVoEGJMT8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEBoCFABGppbxYqSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEJUAFaFBizE8CJEACJEACJEACJEACJEACJEACJEACJEACJBAaAlSAhuZWsaIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJBCVABGpQY85MACZAACZAACZAACZAACZAACZAACZAACZAACYSGABWgoblVrCgJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEBQAlSABiXG/CRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAqEhQAVoaG4VK0oCJEACJEACJEACJEACJEACJEACJEACJEACJBCUABWgQYkxPwmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQGgIUAEamlvFipIACZAACZAACZAACZAACZAACZAACZAACZAACQQlQAVoUGLMTwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEBoCVICG5laxoiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAkEJUAEalBjzkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJhIYAFaChuVWsKAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQFACVIAGJcb8JEACJEACJEACJEACJEACJEACJEACJEACJEACoSFABWhobhUrSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEJQAFaBBiTE/CZBA1gj8+uuvsnHjxqyVz4JJgARIgARIgATCS+Cvv/6SH374QX777bfwNoI1J4EUCXCcnCI4nkYCJEAC/yNQmiRIgARIoCQIbN26VWbPni0vv/yyvPnmm7Ju3TrZtm2brkqVKlWkSZMm0rhxY70+9dRTpXbt2iVRTV4zTwn8+OOPMnDgwJjaDRgwQNq1axeTXkwJmzdvlssvvzzjTR45cqTss88+GS831wUWw7OTL23Ml3rk+hkr9OtNmDBB3njjjahmVq5cWUaPHh2VlomdpUuXyqxZs/RYYfny5bJp0ybZuXOnLhpjhTp16uilQYMGcskll3CskAnoIS2j0PqbsI6Tv/vuOxk0aFDMU3TZZZdJmzZtYtKZQAIkQAI5JRChkAAJkEAOCfz555+Rhx9+OLLffvtFVGfnaylXrlxEKV8if/zxRw5rykvlM4Evv/zS89mZPHlyPlc7J3XbsGGDJxu/71u8fKtWrcpJ/bN9kWJ4dvKljenUA/8rxo8fH/n73/+e7UeC5QckoD40xfQxNWvWDFhK4uxr166N9OnTJ+Y68fonpO+xxx6Rq666KoI+kFJYBPz0B+n0N/lEK+zj5JUrV3q+t1OnTs0nzKwLCZBAkRKgC7waMVFIgARyQ0D9KNHWeRdddJF8++23vi+6ZcsWufbaa6Vly5aycOFC3+cxIwmQAAmQQPgIzJs3T/f3sOiDVReluAgsWbJEmjZtKv/6178CNXz79u0yZswYOfDAA2Xu3LmBzmXm/CVQTP0Bx8n5+xyyZiRAAoVBgArQwriPbAUJ5D2Bzz77TNq2bSv/+c9/Uq7rxx9/LEcffbS89dZbKZfBE0mABEiABPKTwJo1a6R3797SsWNHgRKMUnwE8Ax07dpVfvnll5Qbj4+mZ511lkCZRAkvgWLrDzhODu+zypqTAAmEhwBjgIbnXrGmJBBaAgja3q1bN1HuSQnbULVqVdl9990TWocq1yBRbnHy4YcfFkQ8woRAeJAESIAEiohA586dRYVaKKIWs6luArfddpusX7/enaz3VTgcbd2JmOCw9vzmm2/082JigtonoYy+ffvKnDlzZNddae9hswnLdjH1Bxwnh+WpZD1JgATCToAK0LDfQdafBEJA4OKLL477o7ZFixaCHzxwb99///11azC764svvig33XSTfPHFFzEthPv8NddcI08++WTMMSYUBwFMuHHvvffGNBbPEcWbAKynMZFRqlKrVq1UT+V5OSaQL+9H0HrgA5dbdtllF3cS9wuUAP63qzjOMa2rWLGiHidccMEFstdee0UdV/EGZfDgwfLcc89FpWPn9ddf12FzjjzyyJhjTMh/AkH7g6D9TT4R4Dg5n+4G60ICJFDIBKgALeS7y7aRQB4QQOymp59+2rMmF154oY7XVaZMmajj+IEDK0/M/t69e3dtwRGVQe1MmzZNRo0aJbAapRQfgfLly4uaHKX4Gp5GiytVqiStW7dOowSeGhYC+fJ+5Es9wnLfir2e7777rqjJDmMwTJw4UXr06BGTjgTM/v7MM89Ip06d5O23347Jg7jhVIDGYCnIhLD2NxwnF+TjyEaRAAnkKQH6hOTpjWG1SKBQCNxzzz2eTYFrmpoNXtzKTzsz3OGnT58uDRs2tJP1Ntzf8KMoqMCiAHGlli9f7vlDK2h58fL//vvvgnhOWBCPLJngRx9CBCxbtkx+/vnnZNkDH4eLoJpVV9fHy10wcIHqBMRoA8elS5fKunXrBG3OtPz111/6Gtlg4lXXTZs26fZgnSnB/QejH374IVNFFn05eF/w7MEFFs9IoYmamFO7ASMOJt5bL6VQobW5ENuTiz7ScMt034V+C/+/cvl+ff7556Y5UWtYryeS0qVLy8033+yZZdGiRZ7p8RLx/xH1yPbkWyU9RsjVs5np5zLefct0ei7/xxTrOBn3DP/r8L4hBEAmJBfvbyrj0lzUC/yy9b5l433IFZNMPFcso7AI0AK0sO4nW0MCeUUACj24srsF8bji/Vhx5917773l6quvFswGDClVqpQccsghejIkv+7OcJF7/PHHtZUIlJ/mBx0UrI0bN5bmzZvr2elhkYofUokESlv3zLR/+9vf5Pzzz9enPfXUU/LQQw/piZowsDPSoUMHbbHYs2dPk6TXH3zwgdx9993ywgsvyLZt25xjCAeAMtH2ChUqOOnuDUwqBfc/W+rUqaPbizQoUOAqDovZrVu36mxg2qpVKz3RyKBBg2TPPfe0T4+7jYEVOE6YMEFWr17tqfDcb7/95IQTTtAWvMcee2zcsnDg3//+t9x1111ReXBPwQP3CXVD/DZcF1K/fn254oor5LLLLpPvv/9eT3IRdbLaufHGG6VLly5RyV9//bWcd955UWmwFHn++ed1GhSTmDkYrpfIa6Ru3bpy2GGHyaWXXirHHHOMSfa1fv/99+W+++4T3N8VK1Y4zxzcyM855xy59tprBW6diGWLcA62VK9eXfAc5bMgTiNc9uxn3NQXzy3eCS/Bu4N3yC2wTgX/eB9EMFB+5JFHZK6a2RnKZChmjFIQ72zNmjW1Bdh1110nTZo0cRef8j4mXsM76JbRo0cLwnfEE7Qf7ry24L3AMx1PYKk2fPhw+eSTT7TSc8eOHU5WuIHDvRMWvOinYBm/2267OcfdG999913S92PYsGEx1vW4Du5RtWrV3EVG7aMvOfnkk517YA6edNJJUc+zn3q8/PLLTmgG5HcL+gD7/QND9Onm/bXz4z1Gf55IcO6AAQNisuB5xgRM6Uim+8hc9114Bh944AFZvHix7rfw3sEj4/DDD9f3AO8X/m9mS+J5dOAZOOOMMxJeFhNntW/fXr8XiBVqFvTjyQT9ybhx43R//NFHHzkfLdGvHHzwwXrMgf87+P+WTPJtjGDqm8lnM5vPZSr9AfpWiJ/+xvBwr0vqf0whjpPdbN37GOvifygsX/GBAh+48bsAxg74H4fx/hFHHOE+Le5+Jt/fdMal7ljDmapXNt+3eFCz9T5kikm8ejOdBHwRUD9eKCRAAiSQFQLqhwA0gDGLmp010PWUpUJExQONvPrqq5HNmzf7Phd5zzzzzJjre9UJaYceemhEKQwTlq9+fMeU949//COiFHKR4447LuaY+1rKbVuXrwYXkVtuuSWiFLoJz2nUqFFEKZvi1umVV16JOb9p06Y6v/ohEVEKpZjjdp3Uj7uI+voet3xzQCk9I2XLlk1Yll0uttUP1oiK52qKiFmrGK4x5SnlZURZ9UWUojDmGMpUyi1djvrR4HlcKdFirqOsamPyKmWSzjdz5szIPvvsE3Pcbosa1EZwj5XCLaZsdwLuq7LmiCjlVMIylYI7smDBgoiKUReTTymw3cUG2lczH8eUifYo5XugcpJlvvLKKz2voxT2EWURHHO6UmpHlOLZ8xyldIvJbxKUwiqiXFg9z7PvE7aVAi9y2mmnJXzugjw76gea53Xnz59vque5PvDAA2POU0p4z7yoj/oBH5Pf3TZ7H++HUpR6lodEP2306jtwDfXDNG655oBS0HvW97XXXjNZ9NpPPZRi27Msu732tvoIE3nzzTc9z7n++uujru+1c8cdd3iei+csHclGH5mrvkt9FIwMGTIkoj4meLIx/Nu0aaP7Z6VAjsmnlIXp4NPnqg8OMeXi2uqjXUR9LIsoK7G0r+EuAM+fn/9tVapUiagPle7TY/bzbYyACmb62czmc5lKf2Bugp/+xuS115n+H2OXnWy7EMfJ4Gn6DHs9derUCMYB6kO353GTV31kidx///3J0OnjmX5/0xmX2hXOZL2y+b7ZdTbb2XofMsnE1JVrEkiFAKw3KCRAAiSQFQLK0s1zkAOlU7YFygFlMeh5fTPI8lpj4PXPf/4zgh+EXuL140ZZDkWU1ZHvaykLm8jll1/uO7+yQowo132v6kS8lBhQgGKgmUwJZ9oPpZSysPEsH4kqvlpSRa0py70+/fTT45brNdBUcdwiypInLhv8SIcE+aETb/CowisEahfufSLBM6Ms8+LW3c0G3NWkRDH5w6IAVVaAcZ97t7IVimEot90MsI9+Ip489thjvpQT7nLxMUKFyfAsNsizk20FqHKDjeAjhLv+fvaVtVzcDzZ+2ojnFc+a+1rob5KJ18eeevXqRXCfbfFTj1QUHriOsuyLqTsUz8lEWe7GnNe2bdtkpyU8nq0+Mhd9Fz7sKGv9GCbu58LsK+vgiLIIjcmfCQUo/s8py82Yss218eGqX79+ESjglYt6wnuS7CDePXwsMWX7XStL0Lh9C66Zb2OEbDyb2XwuU+kPzL3209+YvGadjf8xpmw/60IcJ8dTgCpvmgj6D7/v2ogRI+IizNb7m864FJXNRr2y+b65AWfjfcgGE3e9uU8CQQhQARqEFvOSAAkEIqDcWTwHOsrtN1A5QTMrd5rIAQcc4HltvwOvsWPHel7W68cNrM78lot8QfPjHFg9eYmXAhRWn7BaDFKn2rVr64Gb+xqwbFUu8oHKcl9XxWp0F6v3vQaaiSxiwQ0/cCBBfuh4DR5h6QRlt7uuifZhhYRnK55g4JjofK9jXu3NlgK0WbNmERUOIaVFuax5NhsfGuI9H/jhbQRKf6/2Q2kWz6pLhQ7wrcT3KjuRxaVXfi/r4WwrQJU7uycXWNFCUdeuXbuECiEoorzE7/uBDwpeLBJZl6qYpJ79i3Kpj6mKn3qkqvC4/fbbPev+3nvvxdTDJCj3O89z8HymKtnsI3PRd8V7N72ei0Rpyj08VYRR58Wz0HVfG//jYJEKTwo1+ZEvC337QvGeH/d1vPZvuOEGu6io7XwaI2Tr2czmc5lqf4Cb4Ke/sW9Wtv7H2NdItl2I4+R4ClCvdylRmgoHFPdjQ7be33TGpbjX2ahXNt83+/nM1vuQDSZ2vblNAkEJUAEalBjzkwAJ+CaAH/Feg5t0LTeSVUDFcfO8LhRovXr1ikC5OWPGjIiKQxqBtaRXHVXcMz2Ydl/L68eNOR+DtaFDh0bgxgc35EmTJkX22GMPz/LNObCkgqIIijUVeyyiYoV65oc7uZd4KUBN2VjDZVzFJI2oWJSR2bNnR1S8ybhWj8a60r6O149RcBw4cGAE7q5qoozIxo0bI++8805cN95Ro0bZRTrbXgNNu+7YhhJIxWnVSmO4QRsJ8kPHa/BoX0fFF9SM4LYNxQ8Ga/GU1CpOnKlC1Bqu/jVq1PC8d7BoBSvc4y+++EIrs2G9Z9fB3obyPh2J5wJvXyPotoofF7dKKu6iZ1v23XffyE8//aRDOHhZdUEJrWZ9jluul5Uh6q1ig0WmTJkSUbFAIyoGrrbYhnLa3SYol1Ws2pjygzw72VSAworRq4+EC7qK/+nUGxZ64KQmgolpI9qs4qI6ec2G3zauX7/eU8msYj2aomLWUHS6WcPaXMXfi8nrpx6oP6z6sHg9J7DqNMexNuEHvvrqK8/3NFHdvRS+6KPRh6Uq2ewjs913IUxMvBAgeM/wPwPvMN6jJ554IgI3cPe9N/uZUoDCMjneu2+u5bVWMZUjcM3H/9FkAjdcL7d3tE9Nrqjd/NFuFcM8omaZj2kz+pZ418mnMUK2ns1sPpep9ge45376G/vZiPecpfs/xr5Gsm2v/wF4vsM8Tk6mAO3WrVsEHxzxPwMfyDGGiPchFdbebsnm+5vOuDRb9crm+2azzcb7kC0mdr25TQJBCVABGpQY85MACfgiAFc2LyUSrBOzKVDEef04UpOsRGbNmhVzaVi2wYXd6xy4Bbol3o8b/CCCssQtiFvmVTbS4GrqdtOFQtFLCYBYoF6SSAHao0cPz1iIcHf3ig0KJRKUZ7accsopESiD7TaoiYbsLM42lIBeP5ARK9JLEg00jz/+eK0sNOdBMYl4mUaC/NBJNHjs3LmzZ7gDWPjYbTbb+IHtJbfddptn/r59+3paJqmg9p4uvLhO2BSgUOR17drVs/1w7zvqqKM8j3kp3A3bZ5991vOcs88+25Mnng0va1o1gZEp0lkHeXayqQDFDz/zXJk13p944TfwzCBsAizfEN4DH3PuvPNOT0VMkDYiTIW5vllDmRUv7AY+SJh8Zg1XYi8JUg+c7+WSD6VnPMH7a+pg1oksqBHn2eQz61NPPTVe8b7Ss9lHZrvvwkdAw8Fe45lw/28CDNzPePGZM6UAxXVguYiPd3adgmzjfzfel3ji9ZEU7xaU6m7B/zWvcDoXXXSRO6vez6cxQraezWw/lwZs0P4gSH+Tzf8xpv7J1oU6Tk6kAEUoHK//LfE8aPCx3S3ZfH/TGZdmq165eN+y9T5ki4n7meA+CQQhQAVoEFrMSwIk4JsAvl57/WBJV7mTrAJqBmrP62JQk0jU7LGe56kZIaNOi/fjRs3EHpXP7EBx58UB7tfxXMNPPPHEmHNgXegl8RSgUKJ6WWSZMjAZk1e9Hn30UZPFWUPBhYmYMECCog+WMfEEMTzd5cZTYMQbaEI5m8wiK8gPnXiDR1g+xWMERbRXGAH8oPQSrziOaAcs7OIJLITdrLCf7juSawtQtA8c41mSebXx/9g7DzApqqwNf90TSUNGkSCCBEHMqKiIWdRV0FUJ5vC7mDHs6rKuuq67iq4rrCAmjCAgQVERRVFQQAVEQZKgKDlJZvJM139PDd10qOqu6q6eru757vP0VNW956a3wlSfPvccsXQ2U/RJe6IAD68nPMMV9CLrT37rBbEsFeVJv379tOeee85fHNjauXaSqQBV0W8j5ihzFgtGM+sfUdBECyzmn6SdOcozLpy1HIv1X3gSS1QjWQlQZ5TsjEPq21V4iBWR0XiEbXiSZ5iRrIomHy5q+zhZz8hkP7uM/KGKJZa4OTBLck8ZcXRSASp9ixX0kCFDDH8QNOo/PE8s68KDckm78kwOl5VjIxcOIi9J/JaH1xGFqZFrELe9IyTj2kz2dVlF3f7zwM7zJpn/Y/zjj7XN1PdkMwWovCNs27bNEItcp0ZW2eGrn5J9/8b7XprMcVXH/ZaM+yGZTAwvImaSgEUCXvUPnYkESIAEHCegrLEM21QvfIb5TmSqLyNQwX8imjrmmGOgLPEi8oMznn766eDDwL5aOh7Yj7ajrC0Ni5W1jGG++uIJZdVpWKaUnRH5aqliRF60DOVbEGoJsqmIUrJAKYoiypWSICJPWfKiXbt2UMo/PPLII1DWtBEy6ssqlBIEShEWUbZ79+6IvGgZffr0gQp2EU3EkTJltWjKSL2IQykiI/pRgX8i8iRDLRGNyFcKOCi3CBH5/owBAwZABQ7xH6b1Vq415afW0hzk+lFfMqAUzKbyyldjRJmyeIT6AhWR789Q/k2xcOFCKAUh1BcGjB07FirYmL/YdVtljQilbIoYlzyL5LpRrhOgrGTx1VdfQVnj6XJyTaofNyLqJJKhrCj1+zu8DbUUODwLRnkqGBGUVU+EbHVkKOtNKEVURFfKrUhEnnJ7EpGnXFFA/eAUkW83o7qfkU49u4yeW7GeS+pHRkPmdpnFklduFaCUiZD/ScqNCtSPlFGfGeHtyf8dZcmK8GdJ+LG/XrTrQFmURvQt/5PVD5z+6jG3qXpHqM5r06nrMiZMBwSMroPq/h9T096T5f+EPHONklynbdq0iSgKf+cyOm9SKdn3b6z30lSMy8n7zWj8id4PRm1Wx7mKuIiYQQJhBCK//YYJ8JAESIAE4iGgrC8gL3fKyiukurKsg3wxkXKnk/KvCGk/PCl/Q5CXq2hJ+XzSX8zCFbRWv+AoizPD5o0UHCKoosYbykum0Rf6cI6mlfcXqOX1UUVEySrKWWVRFiInDK0kZaGJr7/+GsoiLPAxU9LaHbsK/GJlCAnLmJ0zf8NG16jyx+gvDmyVRQNUIJ/AsX/HTMHtL5f7Qy0nhvI96s9K6lZ+CFDuCOLqQxTCsdJFF10EFSEZI0aMiCqqfNJCBd0ylRFln/IbFVEuSvhoSfnzjVbsujJlBQ557iifdxFjU0sEoaxP9Y8UKutXiKJSnmXycVJxLs9GtZwXDzzwQMg4lGUk5J72P4/kvIhSOTzddNNNEcqhcJlkHQsX+aHhpZdeCuliwoQJ+M9//hPy3Je88CR1RdGWjJTMZ6QTzy55bsmPBeEpVtvyLJB7UQUzDK+alGP5MUC5stA/yqIIcl0qq2X9vlG+iaP2qfwuQ7ktwRdffBGQU9Zpgf3gHbnnzMpETv5nhj+rFy1ahBNOOCG4GdN9M66peEdI1rVpNkc/FKv/U/3yydq65X9MTXtPVitlop5So/cMMWwITmb3aLLv31jvpakYl1P3W7Luh1QwCb5WuE8CZgSoADUjw3wSIIGECMiXahWUAPKiHZ7kS4TRi3C4nN3jcGWev34sxUmwXLwKUOU/099MyFYF2Ag59h8IG7MkX+oTTUa/pIe32bp16wgFqJEFqL+eirQL5ZBe/+KpokWKCxV/UdStmZWDWSUZV3UkI0vW4H7NvpgGy8i+mdLYyjysyIT3F++xWA+qyOjxVrdUT6wXRdmwbNkyQ3mxHhOrgmhJrkG1HC5CxMyaOkKwGjJiXftG4zcalijuxLJN+Tw0Kg7kiRWMCsiifyRTLffHU089hVhfKAMNxNi5/vrrofxBQiy5/Un6nDhxIuScSVI+lBGucBIrcn+5v151b+WaDleArlu3DmoZPLp3764PR/43LFiwIGJoyj1HRF4iGdX1jHTi2WX23DKyfA9nIgr46lKABvctSshbb71V/8g9pgIR4aOPPoIot1XwwWDRwL78wCDPI7+yYNWqVYGy4J1YPxoGy/r3zdrylwdvU/2OUB3XphPXZTCzZO275X9MTXtPjvbeK+fa6H05/H+p2T2X7Ps31rtaKsbl1P2WrPshFUyS9cxgu5lFwHz9WWbNk7MhARJIAQEzxWO4FYWVoSlfbxHKuvB6RspWkYlmbRbchpGc2T/w4HqiKDNbzmtmeaoCCwU3EbJvVidEKMZBrBdNqW60RF75fotoWZQeKgI1VDAbjBw5Esp3qWXlpzRmxiaio/0ZZkukzOTjzY+laDZ6GTfqy+x6trJUOdYLrFF/bs4Tpv379zcdYqwvEVJR+XEzrG+mQDAUTnJmLAWoVatnFVwFytembZcPKpAZ5Avffffd58hMxbWAuLgIT8FL3oP3/XJi9WvkssNfXh1bsaI1srYOdoditPxd6nTr1s2RIVb3M9KJZ5fZc8tv8RsNTDTXHtHqOVkm/1fkHnjooYcglphiGWr2PJ0yZUqg62g/8gWELO4YrTgxqprKd4TqvDaduC6N+Dmd56b/MTXlPVnOYaz/4Vas8VNx/8rYY72XpmJcTt1vybofUsFEzhUTCcQiQAVoLEIsJwESiJuAKM2Mklgi2Enip0xFf4b4mpMvPP/85z+xZMmSiCaMFHoiFG7VGVFxf4aRFZYVaxgrL23hfcry12Qmsy+3wX2qYEbBh/q+CmQRkieWVGKhJn4IjZJ8sRPFqAoCBVHKGPlhsqsAjaYcNhpDvHmxXh6tKqLNrjsjZXL4WI2WeofLpNOx+Hx64oknTIesopZj3rx5puVSYObn08i/bNSGklgYS8EZvmwv2lDEF6hY4z322GM4/PDDo4mGlIlljPg9ff3110Py4z3405/+FFFV7ntZdizPCvHxG55k6bwbkpFls1iv+hXVsh+e5H+KEykVz0gnnl1mimsr/y9labnTSdzNiPsMUWjefPPNEOW6/L+36oamd+/e+v8ho3GpiPCBbCs/TAWEY+wUFRXFkKgqTtU7QnVfm05cl5aAJijkpv8xNeU9WU6Z3dVARqc5FfevjCPWe2kqxuXU/Zas+yEVTIyuGeaRQDgBLoEPJ8JjEiABxwjIi534YQtPw4cPhwThifVC4a8X7HdOlt3J5+GHH9aX8wYHXglX3vnrmy3185f7t0YBIaL56vTXc+NWvvjESkZK0mAFjCgPZKmtLHcPTvISe9111+Hqq6/Wl5gGLxVXEYKDRfV9uy+9we1FNOZghl3FrFnXZu4GrChAZWlupiTxjyrXRDSlgPjpEhm5h81ejjt06GCIRBRxbkniMytaMvIJG01eLLZlCbp8JJDTtGnTdIWj+NgNXpZu1IZYgcoS9kTTmWeeCWEf7LdLngFiWSf3ZLj/W7Hm7dWrV6LdOlJflJmDBw8O8Tktz7c5c+bowcyEY3CSHzfkOkw0peoZ6cSzK/hZH8zBigI0GT/cyP8Zo6BlkydPjuozO3jsYsUc7stWyoOfHW3btg2uou/L9S0+d+2mWFZhdttzUj4V16YT16WTDMzactP/GL4nm50l4/xU3b+x3ktTMS6n7rdk3Q+pYGJ81TCXBEIJUAEayoNHJEACDhKQyKnyy2K4ZaVYE0kgFCvLN0X2hRdeMByVWHwEJ1nCLl9s5cU/OIkvuFhJvtAZKQTTVQEq/u4kErxZEgWOkZI0eDmWBJoIV35Ke88//7weNMWo7fCInSJj9yXNrsLUaBzVmSeKd7HoDVdUicWcRPE2S7LsSJZuZkoSC0YjP4vh8xO3Evfcc0+E30a/nCgDmzZtCgnSEpzE9UK0JEpDeaYceeSRkIBIspWPKHrivabM6hkFj/GPTazj7FiA+uv5t2L1Jp+//e1vejtiMfvll19i6tSpul9Lv5x/K89IUaSbKeL9cla2YgUa/lwWBZSRpYn4/rR7b1sZQ7hMuA+48HI5FmtGUcYKo+Ak0eDlmRb+P+GMM86AFXcMwW0Z7afqGWk0Frt58r9ZlqSGK+vnz58ftSlRhDu5tNHfmQT5kusp/HzLORSrULN70V9ftkb/wyU/2MJJVpKEJ7lfxa+pk8HFwvuo7uN0vjajsQq/PqLJmpW56X8M35PNzpJxfqru31jPn1SNy5iSvdxk3Q/pzMQeQUqnGwEugU+3M8bxkkAaEZBfTO+8807DEcsyWbNl1f4K8qVVglQYWdLJ0nSJhhyc5J94z549g7P0fQneIRHLoyWxKA3/kizyRx11VLRqri178803Q6xewgcq5Ub+y4IVoGKJFp7EL6bRclO/nJFFoxFXv7zRNtaLplGdVObJl3ZZxhyevvvuO92SLzzffyzXXLjS1F+Wbtu5c+caLn0X9wBGvlRffvllvP/++6bT7NixY0SZ3MfBS1nDBcRSUZSkEhDl0UcfxeWXXw6JfB+u4AmvF+3YzKdgsEVZeH0zFx/h94EoksRlgHCQ4FGy7FfcewQneYaKhZAogOQZJlujZGS9biQXK08su8PPlwSREfcWwUnuUYn+7nQyuvfFathKMnouydL3YF+g/nacCn6Uqmekfx6JboOf9/625D6KZsUtLhdE6e50atSokWFEdQlgJJbRsZLcX0OHDjUUE1+7/mRm+SrXuVkShe8//vEPPSiYPGOsXpNm7VVHfrpfm8IokedBLMZu+R/D9+RYZyq0PFX3r9G1GDyyVI0reAyJ7Cfjfkh3JonwZF13E6AC1N3nh6MjgbQncPvtt4dYX/gnJAGLzjrrLDzzzDOGikdZti4+wMItevz1Bw4caPhybPbFVuSNFKnSnnxBfuutt/xNB7ai/DTyaRkQcPGOWLTce++9hl/U5MvrkCFDIkYvVoxijeBPe/bs8e8GtqKwM7N+E6WWkWVQrOXCgcb371j1vRleL5XHgwYNMuxelEThynf58nz//fdDAntlQpLrQZYgG/nFlOjcjzzyiOE0ReG3ZcsWw7KLL744Il/aF1+zRsqHpUuXQpT64alPnz6QH0biTaKUMUpvv/224XzFT6mRL83wNkRZI9blEohHLNn/8pe/YNSoUbriNlp0bbFoNUpi8epEaty4sa44Dm5LeIffwxdccAFatmwZLObIvpGvRLPnTXiHcs2EL0kWRXX4/Ve7dm388Y9/DK8e13GqnpFxDdagkvyPDU/CW6x7je5nub6jWbWHt2X3WFyuGKUnn3xSd3tjdi2Itbg8T4KDHQW3I9erP51yyimGQbPEhYKZ4vfZZ5/V780rrrhCjyYv7jvk/jULHuLvK5XbdL82hV0iz4NY7N3yP0bGyffkWGfrQHmq7t9Y76WpGtcBMontJeN+SHcmiRFlbVcTUC/hTCRAAiSQVAJqKZamrORkXbrhRykoNLX8TVO+uzS1NFZTSjhN/SpuKCttqKWemlKgGo5ZvfRrahmbYV315V5TUcw1FUBJU5FRtS+++EJTy8QNZdWvvZpaChjRh1JURMirZYQRcsEZRvNWll7BIiH7aulrRB/CwyipZb8RssH9qaWemlpCqylrM019udPkXKhlKYZ1lBIvpIsxY8YYyqkvmppahhYiq5boakqxYCh//vnnh8j6D5TS2VBeWZH6RUy36ounYV2lVIyoo5RjhrJqaW+EbHCGsAtmKftynRolpSTSr8tweTlWimVNWStryipRv96Uv6WIdoPrKetmoy4s5ymlomH7cp0qBVrcn7vuustwDEpZYtjfVVddpcvLtaeWdBvKqB8YDNtUCjdNWW0Z1lEWkZpc98q1hqaWvGpK8Wl6z6sI6xHt27l25DpXy2cNx6F+YNDUcne9ffXDgH5vnXTSSYaycn6Vn86QsSh/uYay3bt3N3y+qR8uNGXRGlFH+dkKaVcO7MwxvLLcy8HXo9G+sloNr2Z4bHccJ5xwQkTfavm9phRb+nNbuUPRlLWxYV+SKdeo0XiD8/zXpWkjNgqS/YxM9rNL/l+aXd/KglpT7mP0/x3KtYl+z5n9bxW+yhWIDXLGosr/qKZc2ZieQ2VRrqkfOTXlbkNTrlg0+V85YMAATSkkTeso5WdEZ2+88YahvNx76sfXgLz8z5RrTik/IuSVz9yAXPCOW94RknltJvu69PO0+zyw87xJ5v8Y//jtbDPpPVn5kY64X+QZoYwNoiKx+s6VzPs3kffSZI2rOu63ZN0PyWIS9UJiIQnEICCWV0wkQAIkkHQCasm74QtR8BdTK/uixFR+KaOOVy2t17KzsxPqTwVpMuzDLV9u/IOLpQD1MxUlgrKmMGUiSuhwpbJanmsqf/LJJ2vKEkj/IioKTn8/RltRuhmlRF407XzRqY6XR5mfWhoalYMRm4KCgog6yVKAGvVvJ09ZU0acRlEiG7Uhiorg60n5OjW9/kSRYZQ++ugjw7aN+jPKky9Tyootomk7145UVsFyTMchz5lu3bpp9evXD5ExUpiEK0CVNZsmnIzGrgLEacraTFOWwpr8MHHZZZeZKnlUJPiE5xjewBFHHGE4LhmrKMFE4W8l2WWtLDNN+/VzivbjkbKejVlf+ea1MnRLMsl+RlbHs0vuPz9bo61ci9H+d/jrOKEAFegqcFXC/7/9YxLF6A8//BBxLuVHGbMfAmWuyqWJJj/ORFOsKlcXEe1KhlveEZJ5bVbHdSks7T4P7D5vkvU/RsYeT8qU9+RkK0CTef8m8l6arHFV1/2WjPshWUziub9YhwT8BKgA9ZPglgRIIOkExJJCLCv9X07sbkWJF836J3gCI0aMiLsvtSRVt5YMbs+/75YvN/7xmClAO3fubJmzKGtUUCp/kyFbtZzXcjtyPkWRGn5exXpVLI3CUyIvmna+6FTXy6PMTyyYw+dvdqx8FmpG1pNi4ZxIMrMANRuH1fxwBahaXqypJceG8zWyrlU+Tw1lxXLY7EcNURpaHV+wnFjZBitgg3nauXak3uLFiy0pgPz9n3baaZoKJBQxbjnf4enHH3/UVMCnCFl/W7G2/fr1C29SP7Y7x/BGlC9F0zEpf4zh4qbHdsdhZrUWzEGeSdGSKK+C5YP3RUlnVXkbrY/gsmQ+I6vj2SVfUOUHrWBO0fZlNYdaLh4hL4pxp5LyiRvRfrQxGZXJjxPKhY7pkFTAtogfLozaMcpTrntM23XTO0Kyrs3quC4FsN3ngd3njfSRjP8x0m68KRPek5OtABW2ybp/E3kvTda4qut+k/En435I1rmS8TKRQDwE6ANUvdkwkQAJVA8B9TKOzz77LK7AQuIvVIIUqCVqlgZ722236ZGTjYI8mDUgvgLVcg1IEAijqMdm9dyYL/4E1dLAmEOTIC8SXOaWW24xlFXWZTj66KMNy4IzxT+SMJeo8eLUPziJP1JhWhOS+LT9z3/+o0eFN5uvBE0Sf3PiH1O4hafwIDTh5W45Fj+BaslqxHCUUg6XXnppRL5ENTfyYSl+99SyZChFTESd1157Tb8nlYVlRJlZhvTx4Ycfwsx/p1k9s3zx9Tds2DBDn8PhdcRHoDzjxJemlSRjVa44oJTeVsQDMnINKctUvPLKK4E8J3fEl7LRM1D6TUbwI//YxWerRHSPlsx8OfvrGAVD8pfJMzFWMAu/rNVtuj8jlaJQ/98q/59jJXm2y/8WZZEcSzShcvGRLP48jZ4XVhoW37rK0jeqD+/jjz8eykUGjCIVm/Uh1////vc/3b+2mYyb8tP92nTieRDrfLjhf0zwGPmeHEzDfN+t969bx2VOMrQkGfdDujMJJcSjTCBABWgmnEXOgQTSiMAZZ5wBCfIhikblT9HQyb1/OvJFVRSfyicdZsyYgRNPPNFfZGkrDrjV0luoX9Rx6qmnmtZRPsfQv39/KN+getR5U8E0KpDgAcp6QudsFIlRvshKwCOJFGsUCMM/VQkYonyh6kGT1FJIf3ZgK18IRUEqXzaV1S0k6rdRIAsZS01IotBU1n96NHJR+El0eFGGCUeJsilKYmEugURE8aB+uYzAYsQ5QijFGWrZrGGEe+VPEMq3peHoJMiWvFwbKaC+++47PciIUUVRxsm9KYFrwoPcBMuL0mPcuHH6PR8c9TlYJt79W2+9FcpHG5SPT8MmlGWhfk4loJpdBbYEMJLgYVJXWeIZtu/PlPtNlMvKKlUP3CbBWJKR5IcRUeaGJ7m3lYuG8GzHjuU+kf8PRs8Q6UTuGbnGoiVRcsq1ZpTMguQZyVrNy4RnpFyz8n9S/i8b/b+Qe1b+T8i5URZCVtEkJHfJJZfo97KMSe5tufajJTnn8r4wfPhw/f6Q/VhJ7jdlXQXlUxTRnhny/1IUU/ID35133hmrWdeUp/u16cTzwMrJcMP/mOBx8j05mIb5vlvvX7eOy5xkaEky7od0ZxJKiEfpTsAjZqPpPgmOnwRIIH0JSKRSUbBJdFmJCC1WRyqoB8RyU75oiyLPqaQCpkAtzcHatWv1SOaieJBI705Zijk1TjvtiAVLcIRbf1215ATyq6s/SZRcFdACEhVZ8mXedtmKpZ7yK6YzVMuLdcWnCsqCZClh/GPP5K1YPkpE8eDUs2dP3SIrOI/7BwjIs0ItHdevRbl35XkhVlyifK+OJEoQ6X/jxo36s0P6l5d7I8VuPOORe3XdunWBjyhfxEJUPq1btzZV7sXTl5vryHlW/hv18ywWwAcffLCugBblbLQk/0fkelCBe0LE5IcaaS+ZKVOekfJ8//bbb6ECAkEtbddXXsSyzE0mV2lbBRrDmjVr9Mjrq1evhoxR7n/5gUmU4soXb8L/i+T/m3zkGpJzKT+Oyn0nSmHlrznZU0xq++l+bcb7PIgHaqr/x4SPme/J4USMj916/7p1XMYUI3OTcT+kO5NISsxJJwJUgKbT2eJYSYAESCCMgFUFaFg1HjpM4NVXX4UszRWlvf/TsmXLmErmc845R7duDh6OLG1WfqiCs7hPAiRgkYAKYKe7oQgXF9cUYp3NRAIkQAIkQAIkQAIkUDMJZNfMaXPWJEACJEACJOAcAVlGKf7WgpMs2VQBcTBr1qzg7MD+8uXL8fXXXweO/TvBlrv+PG5JgARiExDr2ZEjR0YIinWuFZ/IERWZQQIkQAIkQAIkQAIkkDEEojvUyZhpciIkQAIkQAIkkDwCRr4hfT6f7r/2xRdfhOwHpzlz5ui+HGVZYnASpan4ZmUiARKITmDHjh261bWK6g5ZIirBp+QHh8LCwoiKVoKpRFRiBgmQAAmQAAmQAAmQQEYRoAVoRp1OToYESIAESCAVBHr06KH7rw33OyhjGThwoB71XZbEix878ae0a9cuw2FKoA3xTctEAiQQnYBYVks0cvnRIPwHhvCasiyeiQRIgARIgARIgARIoGYToAVozT7/nD0JkAAJkIADBCRAyKhRo0xbEms1idwtAXTMlJ8SzOPxxx83bYMFJEACBwhIgBpJsZSfN9xwgx486UBN7pEACZAACZAACZAACdREAlSA1sSzzjmTAAmQAAk4TqB///544okndEtQu42L388PPvhAj2psty7lSaAmEvArQKPN/Q9/+APEBQUTCZAACZAACZAACZAACVABymuABEiABNKYQH5+PmRpdfgnJycnjWeVvkN/8MEHsXbtWjz66KNo1qxZzIl06tRJj/g+f/58dO/ePaY8BUiABKoIyP2Vl5dniKN9+/YYPnw4Jk6cCD4LDRExkwRIgARIgARIgARqHAGPplKNmzUnTAIkQAIkQALVQECWu2/atAkbN27UP+IDtKCgQLf0PProo9GkSZNqGAW7IIHMJLBmzRqsW7cOGzZsgPjfFavQ1q1bo127drpv0MycNWdFAiRAAiRAAiRAAiQQDwEqQOOhxjokQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJpQYBL4NPiNHGQJEACJEACJEACJEACJEACJEACJEACJEACJEAC8RCgAjQeaqxDAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQFgSy02KUHGTGE5g0aRKuu+66jJ8nJ0gCJEACJEACJEACJEACJJA4AQ80HJpVinLNgw0+46BoifeS2S008pSjwFuJLZU5KEaWo5Nt7i1DnseHdZV5qITH0bbZGAmQQHoTaNGiBX766adqnwQVoNWOnB0aEdi+fTsKCwv1qMl169Y1EmEeCZAACZAACZAACZAACZAACegEvEoBemzJGhR7s1GRexCpxEHgsPIdaF65BytyG2CHt3YcLZhX6VK2CfV8pajMa4IyD9UO5qRYQgI1i8DatWv14LCpmDWfRKmgzj5NCfz3v//FVVddZVrOAhIgARIgARIgARIgARIgARLQSgpReVkToEV7ZL+8mEDiIFA58h5oH7wA70MvwnvKJXG0YF6l4p7TgZ/mI+vNufA0aWEuyBISIIEaRaBNmzYQA7hUJPoATQV19kkCJEACJEACJEACJEACJEACKSbgm/AMfO+PTPEo2H0sApVvPILKEXfHEmM5CZAACZBAFAJUgEaBwyISIAESIAESIAESIAESIAESyFQCvtH/hG/sE5k6vYyZlzb9DWjTRmXMfDgREiABEkgFASpAU0GdfZIACZAACZAACZAACZAACZBAqgloGiAfpjQgwPOUBieJQyQBEnAxASpAXXxyODQSIAESIAESIAESIAESIAESIAESIAESIAESIIHECFABmhg/1iYBEiABEiABEiABEiABEiABEiABEiABEiABEnAxASpAXXxyODQSIAESIAESIAESIAESSCUB39wp0FbMS+UQ2DcJkAAJkAAJkAAJJEwgO+EW2AAJkAAJkAAJkAAJkAAJkEDGEdBKi+F7vB/Q9ihkD/824+bHCZEACVgj4PthJrBzkzVhv9Se7fqe/IjiqdfQnxt7m50LT7cL4MmvHVuWEiRAAiRggwAVoDZgUZQESIAESIAESIAESIAEagyByvKqqZaX1Zgpc6LWCWg7NgNFe+Fp2d56JUqmHQFt82/wDb4g7nFrL9wHu+GbvDc/Cc9ld8fdJysCvs/fhqfjifC0OJw4SIAE9hOgApSXAgmQAAmQAAmQAAmQAAmQAAmQgC0Clf/sC/z2I7ImbIEnO8dW3XBhbd8uaB+9DJSXhheZHmsV+xXzytLQN+ZxUznDgnqN4LnoT/BkZRkWp2OmtnUttI2rbQ1d27ZBl9d+XQJf7QLDupqmKVa3QNu780C5B/A0PBhQ1ppmSft8DCBKclXXU6uemVhkvrqWPD2viMx3IGf9wHHYO3WpAy0Brd68BnXP7OBIW043oq1dDt9/boLn1EuR9be3nW6e7ZFA2hKgAjRtTx0HTgIkQAIkQAIkQAIkQAIkQAIpIlC4C1BuEiCKyEQVoLPfhe/1h+ObyN4dSgH6L9t1s7qcArQ7xnY9t1aovOd0tUx9S1zD08b807aVJs7oh6y/vGbaX8WPX+oK0Ky+f4GnSQtTueosqNxeiPL16rp1IPmK91vIO9CW4034f0jwbx3vgA2SQHoSoAI0Pc8bR00CJEACJEACJEACJEACJEACGUHAc1Z/eHPzlAWodXcLmlLuaM8PAhocBO91j9rjoHxSejJI+SmT9/Z7ENrKBbY4aD/NB9avBLqeDk+zVjbqeuA5+yob8hQlARIggdQToAI09eeAIyABEiABEiABEiABEiABEiCBGkvAk5sPz1kDbM1fKylEpShA6xTAe/71tupmorD34oG2p1U58h5oSgHq7X07vKdcYrt+ulY4uPMceLPLsXHxGek6BY47DQhoxfvgG/skvOdcDU/rTmkw4swfojfzp8gZkgAJkAAJkAAJkAAJkAAJkAAJkAAJkABQcPBvqN/8F6IggaQS0BbPgjbxGfimjUpqP2zcOgFagFpnRUkSIAESIAESIAESIAESIAEScCUBbelcaKVF9sam+XQfnr6Fn4XWU4Fv4IsSuzsoYI5WqdqQj1nyquXSHhU1hwmazwdt9mSgcI89GuJrVZ0T37RX7dXLrwVPj8sTDlJlr1NKkwAJ6AQqK6tA+CoIxCUEqAB1yYngMEiABEiABEjADoGK+8/Wo9dmDZlupxplSYAESIAEMpCA79up8P3j8vhmVrQHvocutl23rKgOcmsDS+v/GVqleRT4g/55EQ56qJft9jOxgrZkNnxPXhP31HzP3W67rldFavf0+KPteqxAAiRAAplGgArQTDujnA8JkAAJkEDNILB6EbQs/huvGSebsyQBEiCB6AQ8R5wMz8W3Asovpp2kfTZaj+DuOaNvSLXixRtQvGBdSF7wga8iB3WbmpcHy3L/AAE5T95bngaKdh/ItLDnm/gsoKx7vVf9zYJ0kEhebXi61Tzls1g0ezqcAE/dBkEwqmdXq1TWfsrS15OTWz0dshcSIAHLBPjNyTIqCpIACZAACZAACZAACZAACZCA+wh4Choj69b/2h5YxRfjgFr1kHXPiyF1C//9Cba8/mFIXvhB3abrw7N4HIOAKMU8fe6IIRVZ7Jv6MlBWDO8AmwrQyKYyPkdbOke3aPb0vgNZf1LK5mpOlQ+cD+zehqxhc4B9O+Fp1rqaR8DuSIAEzAhQAWpGhvkkQAIkQAIkQAIkQAIkQAIkQAI1msD6W8aiaMFaRxi0futa5Hdp7khbbMSYgFa83wq6ZJ+xQLJzN6ngSkoBKq4OtEUzkTV2HTy16yW7V7ZPAiRggQAVoBYgUYQE7BLwTXoW2or58P51NDxer93qlCcBEiABEiABEiABxwloxUohUFFuuV2taG+VrK8S2t6dluvpgnkq+Epuvr06lCYBFxIoXbUVJd87Y+3qKyx14Qw5pGQQ0Hb/DpSr8y2KWCpAk4GYbZKAbQJUgNpGxgokEJuA74vxgPLPh707gPpNYlegBAmQAAmQAAmQAAkkkYDv6w/g++eV8fWwYRUq+x5ir65SgGa98D08Bx1qrx6lM47Alsc+xtYnP9XnVeuYljh89j0ZN0dOiARIgARIwP0EqAB1/zniCNOagJbWo+fgSYAESIAESIAEMoOAp2lLoP1xyiKpzPqElOUn1i4HcvKAFu2t11OSnoJGyuqpwFYdCmcogXJlQaw+krRiG9dfhuKocdPyeGrclJ2esCbBzeR5bDH53QBIQCataJM0xfYAAEAASURBVI/FWvvFJHAWg2zaY0bptCFABWjanCoOlARIgARIgARIgARIgATiI+A5/FhkS1AOG0m+OFdefhBw8GHIfn6+jZoUTSsCmg8Vd3avWrm0f+ANdpegXs+iqNPIya/ytdjutIlKzpqSy5OXjYr/G4Os+16Bp9OJUdtnYXoT8HQ9HZq43CjgarhEzqTvveHwvfTn+Jr4bnrVM9xO7YYHIevV5fAoK34mEsg0AlSAZtoZ5XxIgARIgARIgARIgARIgARIwDIBtWJp869A4e5AjSy1l1U7cBh1J7f2/qAzUaWCCjfvg6b6sqYyDaoXsbu/hRgWhrunLIavMH7L04ot+33hRvTPjGgEsm58XBXLhykhAge3Adp0ASqtW4CirATY8huQXxcQ638bydO8LZCda6MGRUkgfQhQAZo+54ojJQESIAESIAESIAESIAESIAFnCXiykPXOBrXE1hdod+uTn2DLQ1MDx0Y77XpMQl7d3Vg+XUW7rswxEonIq3Wc8gE6/wFHgoR68tVS3XOuhqdVx4h+gjM23jUR5WttBvEKbiCD9z1tjoQmAcsOaZfBs0zvqXlP/gPkYydpv/yASmXV7TnqdGQ9OslOVcqSQEYToAI0o08vJ0cCJEACJEACJEACJEACJEAC0Ql4xIoyS+w+9yelFAW8/qMYW5GzLuvxWpWN0a0qzrr35dhClDAl4L3gJnh63Qj9/JtKubdA93H5yetAkJ9Lbf1KfcDar0vgm/jfkMHXLZuP7MM2Kh+Xamm+uuQbH7Y4pDzWga8yG7vWd4Dmy2w1igTNQ34deI89KxYS15dr+3ZB++YDeHpeCY/4s44zaeWlyif2CuXI+MAPRTGb2ry6SmTXNmg/fx9TPESgWWvlS7txSBYPEieQ2Xdu4nzYAgmQAAmQAAmQAAmQAAmQAAmQAAlkJIF0VX7KydB+nA3f8DuNz8vKBfCpT3Cqrw7qdzqQc1CneQcOLO5VlNXC3s2HWZROTzHfk9coUE3hfXNVek4gaNTa1Jfge+MReLNy4DmzX1CJvV3fc3dA+2y0vUr7pbWvJqFSfWylpq2Q/UaVMt9WPQpHJUAFaFQ8LCQBEiABEiABEiABEiABEiABEiABEnAbAU/XHvDe81KoBaiy0tOmvQKoIFteZfUXnLY9M0N3h9C0w3dKIVaBLctPCi6OuS8WoPu2ttblSldtiymftgKVykJWPhmQtDJluSlJLDgTSJ5T+wDKmhSa8plsMWk7NgOrvgMOOhTibsJO8nTsZkecshYJUAFqERTFSIAESIAESCAZBLS9O+Eb8zhQYjOIhLzIVZShcuhAe8OqVRfeq/8OTx2xg2AiARIgARIgARIggfQk4FFuGzznKmvFoORbMF1XgHoO7Qxv79uDSoDSSfWw66v5aNJukfLaUIEda+wppYIby25cJ/iQ+xlOwHvSRYB8bCTf3Pfhe7wvPCdegKxbn7VRk6LJIkAFaLLIsl0SIAESIAESsEBA+2k+tPeftyBpLKJNf8O4IEquduKF8GSAX6coU2QRCZAACZCAFQLtjoandj0rkpRJIQFRoED9YMqUGAFPTpCf28Sagifbvi/bn7s/k2CvVdU9tXLQ7vO7HGmLjaSWQOXuYvzU6Z+ODCK7eX10WPiAI21laiNUgGbqmeW8SIAESIAE0oKA94Tz4Hl6BrTSIlvj9T12hbJeyIL3oXG26nmUU3tP5+626lCYBEiABEggMwlkP/tlZk4sw2aVdffIDJuRten4Jg2Fb+wT9gLPVFbojYu/xoovJ4Z0dFBJOZqd61OvT1XLuzueG/ojcsnuJlgzr8rKz5tdioM7fw1vdllIG/6D/C9WoHJlA//hga0sk/apQDkbf9bzKv97Czy5+ZCF043KzIMuaWp5/ZYVJ6GiNLZlqadO7oH+LOxtvGcS9k5XAXwspratNVT+vg+/dPlXRI2WL/ZDndPaReQzIz4Cmk9Dxea98VUOr5VlXykf3kSmH1MBmulnmPMjARIgARJwPQFPl1MkGKmt5FPKT2Rlw3vcObbqUZgESIAESIAESIAE0ouAnbckv6xs/fv+2arjYBeOwft+kf3b3Dq70aBFlRIzrKjqcN1aaOsMS6oyC3dXbRd+Fuiy4KAo8qqoVoOtyvVmPjYv647iXTGEozcVUlq2bidKlyl/lFaTuDmt8BnWqdxbYrUVypGA6wi4TgHatWtXDBgwANdccw1atmzpOmAcUM0j4Jv9LrB7m72J7/ldl/d9+hY8yt+e5ZSdC8/pl9urY7lxCpIACZAACZAACZAACZAACaQ7gZIVm6GVKytDB1J+l4Ph8brTcsz7x0GQj50kPkB9D/eG55yrEG45u+6mMdj56jfocNYYZOWU4KfPrjNtumR3M/w86wrk1NqLVsd9pixBqyxLTSs4UJBbe59qZZ/ep5MKUAeGxiZIICMIuE4BunLlSgwePBgPPfQQzj77bFx//fW49NJLUatWrYwAzkmkFwFt/Ur4/j0g7kFrr/4t8Iuf1Ua8KhCKJ8xht9W6lCMBEiABEiABEiABEiCBRAl489QKg3p5ejNapQatyHgZcKL9sH58BFaf/RwqNu6Jr3JYrS47hyCrQe2wXB4KgbKi+vrnpxlXw6Oixgenli8PQP3Ljg7O0vcrbzkG2u7fUe5pgVxtPTbl/BU+T4EePXzvtGUR8hEZPi98lfaWuEe0wQwSIAFDAq5TgPpH6VN+Mz799FP9U1BQgCuvvFJXhp566ql+EW5JIPkEWrSH9y4VnGTXVlt9+T58AdixGZ4r/wzxt2c55SgL0LP6WxanIAmQAAmQAAmQAAkkjUCuMkCQ95j6TZPWBRt2J4Gm950N+UgSa8OVR0T6AnTnyDkqEgglUF6mISc3fCl8qEysI82XDfmEpLwCeOo1CsnSD3RrWg8qd6ql4spF6N5P1ii/nn4Fc36kPHNIgASqjUDYXVxt/Zp29M033+DNN9/E2LFjsWXLFl1uz549eOWVV/RP+/btcd111+lL5Fu3FucUTCSQPAIejweeXjfY7kBfNq8UoN5L74SHXxps82MFEiABEiABEiCB1BPwZOcg66UflBLUhjuf1A+bIyABEiABncAPc8rw06Jy9L3NhkEK2ZFANRGoKI/ihLaaxlDTunGds49jjz0Wzz77LNavX48PP/wQffv2RX7+gV9KVq1apS+PP+yww3DOOedg9OjRKCqyFzm3pp1kzpcESIAESIAESIAESIAE4iHgadISnrrKjImJBMIIaL4saJoElXHdV8qwkZoferwVqN1ooxKgIsKcUnqWaMpF6jsjC/HFuyXYvK4yPScR76jrNlTxn9S9Wb9xvC2kdT3xA1txy9HQ1i539TzGDCvE+p9DXSu4esAZMDjXWYD6mWZnZ+Oiiy7SP7t378aECRN0y9DZs2erf7QaZIn8jBkz9M9tt92mL5EXy9AePXr4m+CWBEiABEiABEiABEiABEiABEggCQQ2LO6J7NwStTQ4KwmtV0+TjdssQbOOC7B2wXnYty3x1YW1jm+FVmPMA+vkHmqwZLp6plrjetnwayW2bawKFDX++ULc/YTyw1lDkuegQ5H14iKgSYsaMuPQaWpL5wAqloe28rvQAhcdrVlZgTnTSrHtp3Kc56JxZfpQXKsADQZfv3593Hzzzfrn119/xVtvvaVbfoo1qKS9e/di1KhR+qddu3b6Evlrr70Whx56aHAz3CcBEiABEiABEiABEiABEiABEnCAQOnexih1oJ1UNuHNrgru5N8mOhZv7Vzkdzwo0WZY3wECa4Ms65Z8W44l88pQ34F206UJT8v2+lC1fbvg1fYgK9fuqlnNuE7h79CUqzfDlF8bntrJVTRr5RaeOpVVVpWabH379yvKYaluYGLKFZ6Kz5GsNG54oTLsA35bQQvQZDE2ajctFKDBA5el7w8//LD+WbZsGd5//3398+233+pWob/88ote9uijj+pL5P/v//4Pffr0gViUMpEACZAACdQMAr7CUqw+XwUwcyBlN6mDNu/d4kBLbIIESCBZBLRKH5bUvteR5r0F+eiy7UlH2mIjJJAJBPI7HYyuvv85MhXxr89EAtVBYL8OLNDV+BGF+L/09dYQmIedHd8PM+EbfAEOkUpVMc0sV8/OK0HHs9+OlH/1bVS+Gpmt53izkPW/ufC0PcpEILHsyr9dBO37zy03og27NSCrDb8TlepjJ3nOvx5Zd4+0U8WS7LzPS/HzEio+LcFyWCittYKdOnXCrl27sH37dqxZswYbN24M4JEl8tOnT9c/bdq00f2G3nTTTYFy7pAACZAACWQuAa3Ch6I5qx2ZYHbz5P6S7cgg2QgJ1HQCyoxCK3PGx5tT7dT0U8L5ZxYBKi4z63xm8my2bqhEE4MJbl7nwyZUopYq272xHZyy+jXoyjVZnmatgCNPQ9myNSoqfbHlceUX/K7c4npQoqy8w1Nu+6bIqi8UI5Nu/dmgWWSBUzktlFXrpl9jt7Z3B1C4W/lAVVdChVI0Fu4CCtR+7Xqx6/olJBjywYf5j+Laeg5qDSilsKd5u0D9slINk16ya40bqM6dBAmknQJU/H9++eWXePvttzFlypRApPhgDl27dtUDJ82fP1/P/u233/Tl85MnT8akSZNCgioF1+M+CZAACZAACZAACZAACZAACZAACZBAehKY9X4J/mgydFkW31GVbVlxsolEZmV7DmmH7Kc+xfrLX8GeuconqMV0RK9RqCzPx69z+0TUaPPQQBRc0CUivzoysm4baqmbyjcegTb+KXhvegKaUphqY/8N742Pw3ueuX9eSw3bFPK0OwZZEzbDU6tuoObH44qxY6svcMyd6iWQNgrQpUuX6n4/RfG5du3aCEqNGjVC//79ccMNN+D444/Xy5cvX44XX3wRI0eORFlZGT766CPcf//9GD58eER9t2YUFxfr8164cCF++OEHXeErbgCOPPJIDBw4EEcccYSjQ3/vvfcgrgWsprZt26Jfv35WxSlHAiRAAiTgFAFZRsilhE7RZDskQAIkQAIZSqBB/+NRub0wMDsJPrJ2VZXFuLfuWmUtuDhQxp30JiBLizeuUefWxHVjZXl6z4+jTz8CwcrPHVsr8YlSgDKljoCrFaCypH3s2LG6AlCUf+EpKysL5557rq707N27N/Ly8kJERDk4dOhQnH766fjjH6t+BxIL0Oeee059Z3S//xmxYJVgTitWrAiZlwSC+vzzz/V5XH755XjllVdQUODMEs3HH38c331nPVraeeedRwVoyNnhAQmQAAlUDwHvgL8B6v8gEwmQAAmQAAmQgDmB5k/2DhSKAuLt63ahrFNVVkH2EnTC9EA5d9KXgCwtnvhiEZqn7xQ48gwnINdnmYUYThmOIaXTc50CVCK6y1L10aNH60o+8eUZnjp06IDrr79eVw62aNEivDji+LLLLoNYKq5evRqbN2/G4sWLcfTRR0fIuSlD5i/WrBXis0IlUdi2b98exxxzDEQBumjRIt2qdcKECVi5ciWmTZuG5s0Te9xLX0uWLHETBo6FBEiABEjAhID3j4NMSphNAiRAAiRAAiRgRIAKCCMqmZH38dhi7NzmQyNPHX1CpajaVvvslM9HeKo32lJJsaasmivQ4aicap8uO7RG4Ocl5Zj/RZk1YUoljYDrFKAtW7bEnj17IiZcr149XHnllbpS8NRTT40oj5XRsWNHXQEqcl5v9T6QYo0tvFyUtHfeeWdA+dlGBXF655130K1bt4DounXrdMvLuXPn6spQWf4/c+bMQHk8O+IyoLS06icJURjfddddMZs59NBDY8pQgARIgARIgARIgARIgARIgARSSYAKiFTST27f+tLi8VVLi3/RTsT/ysbgN98xye3UpHXvjf/CnonfwLvjPV1C8yVf9zB1dBG+n12GR0c1QHa2+1e6mqDL2GyJYzNu+AE3HBk70TSYmOsUoCUlJQFsYvV4xhln6EpPWcJeu3btQJndHfEbKu2JgtVpv5l2xxJLftCgQXp0e5ETC1dZki4+ToNTq1atMGvWLF0pKu4BZF/8ow4YMCBYzNZ+sJuBs846C3fffbet+hQmARIgARIgARIgARIgARIgAbcR8PmogHDbOXFyPOGWvct9PQ2bX3LIcVjfsI1hWXBm80OzcOFVxpHO/XK1ux/m3w3Zes/sh9IvC7B72Rbk1t6tBxMKEXD4YNvGSnw2sQQVyr/p5++W4Lwroo/b4e7ZnAUCc6aVYs3KKr/DFsQpkkQCrlOAylzFqlCWuF933XWQgD9OpKlTp+pLxHNzTTwiO9GJA21s374d48ePD7T0zDPPRCg//YXZ2dkYNmwYevasesAPGTLEMQWoP5CUvy9u7RHwnHQhUK8hUDdUcW2vFUqTAAmQAAmQAAmQAAmQAAkkSmDux1RAJMrQrfXtWPZuqd8K8omVflKLIo9oXRfHnR4aYyRWveDykj1NIJ9kpwkvFOnKT+nnwzeL0f3cPNRrkHyr02TPK1PaLynSMHlUUaZMJ+3n4ToF6CeffKIr9JwOUpQuS7WDI7CLT8++fftGvcgkwNMhhxwCCRj1448/YuvWrWjWrFnUOmaF33//faDohBNOCOxzxz6BrGsetl+JNUiABEiABEiABEiABEiABBwlQAWEozhd1VgyLXtFsdj15Fzk5KZ2SXnjQT3R6IbuIdw93qoxLV9Yri999xcWF2p479UiXHNvXX8Wtykm8OFbRdi7UzMdRWlOLbzQ48GI8iylqRs8ogGatbChzHa5q8eISaYgw3UKUFnyHm+SgEkSIEiipl900UXxNpPSesEK0M6dO1saiwSFEgWo+JaYMWMGxB9oPMm/BD4nJwddu3aNpwnWIQESIAESIAESIAESIAESIAHXEIilgHDNQDN4IL5KDcoLgeP+KZNp2fv7Zh+mTyjGRVfF74bPiVOa07w+ah0VGfhZmI4fEelX8quppTjjkny0OvyAqqflyL7w/edS68O5/VVkNa2LTr8+GlEn+6B6EXnJzPAt/AxY/o2tLrQls3V57ev3oe3dVbX/zYfwbVsXvR3lMtHT7QJ42h8XXc5i6dYNyj3BpAMuHs2qlWfnRxQpjwaY+FYl7vx3ioJ5RYwoMzIO3BUumY9YL5aVleF///ufHuXdzrAkSNKkSZP0Kjt27EDDhmoJcpol8VXqTxL8yEpq0uSAab1YgcajAJV+d+7cqXd35JFHIi8vD+Xl5ZDASPIpLCzEUUcdBSnLz4+8Qa2MkzIkQAIkQAIkQAIkQAIkQAIkkAwClUOug7ZoZkjTPuV276w9PpxlspI5D1UKpIOP/BrNOs8PqSsHSh9iGtB7T+GJ2LXv9JA6uYcf+F4WUlDDD778sFR9x9cc9U9ZHZa908YU49Re+WjQ2IYVXjWd61mK6YZfI/1KKpsoPeDOn4fWD4wku6lSWjYNHMbcqRAD0ywvcto0jimbbAHfc3cAW9bE1Y329QeBeppSgMonVvKsmIesx6bEErNU/s7IQlRWWBI1FFr8TTmWzC/Dkd3c7cbRcPAuzXSdAnT37t26AlSUoHZTsH9PUeilowI02OepRHq3klavXh0Q27Wr6heOQIbFHb/1p4iL9efIkSPxr3/9Cxs2bAhpQfyOSkCq4cOHI1jxGiLEAxIgARIgARIgARIgARIgARKoTgL7lDHH3h0hPWpKPxTNfsqDKgWSN6scnqxIZZI05s2qUoSGNKwOGl95DJrdFLl0NVwuGce1jmuFilb7nGk6W00wialonw9TXitCpcLrpH/K6rDsLVXGe5NfLsKND7prSXnh3iqmZqdt5eIKLJhVihN6mmj+zSq6MD/roXHQVh1w1WdliL65SoG54BN4zr0W2u7fgXkfwXPO1fB0PiV6dbEAPaoqvkp0wdilyxaUYdFcseNMLI0fUYQjRuUgK0u00kyJEnCdAjTeCW3evBlfffVVoHo6Kj9l8MER6hcvXgxZ1u+N4stBFMWrVq0KzNtvxRnIsLgT7P9z9OjRePPNNw1rVlRU6EGaZs6ciVdffRUXXqiC/TCRAAmQAAmQAAmQAAmQAAmQQAoJZP3z/ZDeRQHx7F/2huSFH/TJ/jfOz34er5cPw3yf8RLhjsdk4/7/HrCmC28jFceHfTAwFd3G1ef7rxdj354qH4hO+ae0urQ4rgGHVfp6einO7J2Hw47ICSsxP2xwzYkwixJvXsu4JK9DZHwPYVq4n6lxLbV8WvkwPbp76n2Ymo3Par6n3TGQj52kbfkNmihAu/YANv0KTRSgR54G73nX2WkmbtlKcU/wvDOBjzavrcTMKSU4+7JacY+HFQ8QSIkCdO/evejRowfE2jM8+S0/H3zwQd0CMbzc6FjqSPAfUc5Jatq0KVq3bm0k6vo88fspCk9RfIpS9/nnn8cddyizb5M0dOhQCE9/csICVPquU6cOxB+rfGRMK1euxOzZswMuBrZs2aJbgi5atAjig1SSWIvOmzfPPxRbW2mfiQRIgARIgARIgARIgARIgAQSJSAKiHHKcsqJ9NMPFfjuy1Icn0BEcCfGkY5tbFLKmy/eO+AD0cg/ZTzzSnRpsd0+xw0vwoPDC5RLBGtWeHltm0A+yUib1lToCrFYbW/f4sMn44vxh2vi8GHa8GCgfnLGH2vcTpd76jWErn6v29Dppk3bm/V+CTb+ZmxRblopSoEovE86Ow9167vPFUOUYbuyKCUK0Hr16uGmm27CXXfdZQpl+/btkE88qU+fPvFUc0UdsVy9/fbb8dxzz+njGTx4sL4kvWfPSFPsOXPm4PHHHw8Zt18JHJJp4SB4CbxElZ8+fTq6dOkSqCmWnoMGDcLHH3+s+2bdtm0bSkpKcPPNN2PWrFn6PwOxwI3H/6h00rZt20Bf3CEBEiABEiABEiABEnAnAa28DJ4c+iNz59nhqPwExGJq0xrnFBBiTXeUCyKC++eXLlsJ0qNsawLJyD9loNDijlNLiy12p4utXl6Bbz8rw8nnpn5JuSyJDmYabR7TxooP0zw0bBrp5mDbpko1p1JDBWnW8G+VD9CUqIqiTSeuMs/FA5HV+WR4OpwQV327lQqVz+EpSmHpZCrap+E95Ubi6kHucsXg5Byrq62UXdW33XabvoQ6WPGW6KTr1q2La665JqA8TLS9VNUXpebEiROxadMm3brzrLPOwsCBA9GrVy/d2vKXX37Bp59+qs+zUjlTEStNWZIuqUGDBvrW7p+nnnoK4ktUfKfef//9CPZFGtyWjEGUs/369dOzRen57rvv4rLLLsOJJ56IV155JVjc8r4swR8xYoRleQqSAAmQAAmQAAmQAAlULwHf7MnwPXkNvI9/CO8xZ1Zv5+yNBCwSEAXE+284q4BwS0RwiwhcIbbo6zIsnR/pA1H3TzlT+ac8w74y0UnLXruQJilfoMeelou8WtasQO22b0VeZ7ogkqlZ3TJlfCvjvnlwZOT2CSOL8OO3VUrdJgeHKkg9GWL9KVw8ositJuWn9Cf+bov2Vrl8kGOnkgQSO+OSfLRsmzIVnlNTSWk7KaOXlZWFzz77DGJJGJwk0rhEH3/sscdwxRVXBBcZ7osZukQlr127Nho3bhzVX6ZhAy7MLCgowJgxY9C3b1+djyxJl6Xw8glPQ4YM0Zf7+xWg9evH55/GCmt/3zKuYcOG4euvv9az5s+frytAxYozXkvOl156yd88tyRAAiRAAiRAAiRAAm4ksF75nRfTow0/A1SAuvEMcUyKQLIUEHpE8PNVRPAmXIYa60KrqNAwQUXANksTX1T+KU+x759y4ZdlKC3R0Ogg83Owb5cPZaVmPcefv+t3Hz56uxiX3hTHkvL4uw3UrCjX8M7z5kwDgmE7Yrl6Zp9ytOt8wIfpiu/L8f3sqqDTogi99R+RCtKwZnhogcCGX5V7gg+ScPGpvjX1r1csqu97Jj59j4Xh1wiRlClAha4oLOUTnPx+NZo3b45OnToFF9Wo/TPPPBM//vgjbrjhBkybNi1i7mLpKcrPW265Rd/6BeK1APXXt7rt3r17QAG6ZMkSq9UoRwIkQAIkQAIkQALOExC/bA5FSPU41I7zk2SLJEACsQgkUwEhEcEnvVyIm/5KZVGs8/D55BJsWR+09j2sQrz+KbudmQf5mKWVi8vx9KA9ZsUJ509/pxg9LspDuMVkwg1baGCGYrp1gznTaE2ID9PBI6p8mPp0/7gHFKkLvyrDT4vK0fHoAwrSaG2xzJzA1NHFqFM3eRbC63+phLiA6HwC3dCYn4XoJSlVgBoN7YUXXtADAJ122mlGxTUq76CDDsJHH30EWfK+cOFCiLuA3NxcXTF88cUX61avAmTNmjUBLgcfrBwWV0Pq2LFjoBcGMAqg4A4JkIBLCHjr5aH9wr84MhpPTuiyIEcaZSMkQAKOEvBkeXFUxf8cbZONkQAJpB8B8Y8ollLJSt98WmVN19ZGRPBkjcWt7e7Z6cOHb8V2QRDNP2W8c/td+bU8v29+vNUt1dukgttUtwLUKlOzCfy2ogISzf4UZcEsS6k3rA71jztueCH+/mJ9tZo2eco7s7FlUv4tf+ePI24/n65TgIrFI1MogXbt2kE+ZsvUf/5ZLUPan0455RT/blK3RUUHoipK0CQmEiABEnATAY/Xi1rHtnLTkDgWEiABEiABEiCBJBMYNMSeAsL3ulKWTYKy6qyLm89oZGl0HvPV15bqZ7rQu6OKUFwY2wei7p/yJeWf8m/2zlk0fqLgy8QkTEuKYjONNvfJyhfoEcfl6C4iwuXEsvCrqaXoeXFm8gufL49rLgHXKUBr7qmIb+biQ9Xv/1N8oR5//PG2G/r888/1oEtbt25Fq1at8Oyzz8ZsY8WKFQGZmuyqIACBOyRAAiRAAiRAAiRAAiRAAikl4LXpwkJT7jNErSRKTbt1UzpRl3a+dlUF5kyz7gPx2xnKovbSUP+ULp1ayoZll6nZQHfv0DDi73uxb4+xIvW9V4uUe4Fc1K5LDb8ZQ+anP4GUKEA//vhj7Ny5U6fXsmVL9OjRI0By/Pjx+hL4QEacO/3794+zZmqrSQConj17Yv369SgsLMTGjRuRl2fu5+TNN9/Ug0bJqMUvZzRZs5mVlJRg5MiRerEoUf/973+jVq1aZuKQyPMS/d2funTp4t/llgRIgARIgARIgARIgARIgARIoAYSGKeCtGjG+jVTGsH+KU2FanCBLE+3y9QM15qVoUvfg+X27dbwwRvF6Ht7neBs7pNARhFIiQL0wQcfxKJFi3SQvXv3DlGAXnvttSgrq4pIlgjpdFWA5uTk6H4+161bp0//s88+w0UXXWSIYt++fXjxxRcDZX/+858D+3Z2ROEqvkWFuyhD33jjDQwcONC0Celz2bJlenmTJk0g54yJBEiABEiABEiABEiABEiABEigZhKY/0UpVi2usD35YP+UtitneAWd6Y/2mcaL5Yv3SvRl8Ae3pv/7eBmynrsJ0L7ZhefnwgsvDIzq3nvv1S1BAxn7d/bu3YtevXph1apVeo74/rzgggvCxfTjl156CU8++aT+kaBK4alOnTro27dvIPu+++4LKKgDmft3PvnkEwwePDiQ/fDDD6OgoCBwzB0SIAESIAESIAESIAESIAESIIGaQ6CsVMNE5c8z3iT+KUuKbZqOxttZmtTTmb4YP9N4pqkWemL88wcixMfThqvqKBcXTCQQTCAlFqBicdi6dWt9HN26dQsej27tWFFRfb9yhHTukoPbb79dt8IUK0uJsC5+PR955BF9+/vvv2PGjBkQVwFLly7VR9y0aVO8/PLLpqN/5pln9HZEQIJMBStY/ZVGjBiBb7/9VpeTAEcnnngiRPkqsuKmYMmSJXpEerH+1Pbb4J999tlRLUX9bXNLAiRAAiRAAiRAAiRAAiRAAm4j4MmvpfsARV5ttw0trcYzfXwxdmzxxT1m8U/50ZhiXHYzz4Mf4ifCdGv8TP3t2N0umVeOH78pQ9eTc+1WdY28t1sv+FbMg6fLqa4ZEwfiDgIpUYAOGzbMdPaTJ082LaspBWKROWHCBJx66qnYtWsXfvrpJwwYMMBw+rIEXRSinTt3Niy3mlmvXj0I+z59+kCiystyeL/VqFEbohx96qmnkJVF83gjPswjARIgARIgARIgARIgARJwNwFPnzvhPbQLPCcZuxxz9+jdMbod2yoxbWxxwoP5dEIxelyYh6aH8PulMP3YAabxnpTxIwtxxAk5yM5OTwtKT+fuyHpiWrzTZ70MJpASBWgG83RsaqLQFMXnQw89hFGjRkUEhhKF5R133AFZrt64cWNH+pVgRmJVOnToUD0Q0u7du0PaFT+hRx11FO655x5ThWxIBR6QAAmQAAmQAAmQAAmQAAmQgEsJeGoXwHNqH5eOLj2GNUktfS+zHvjddFIV5cCEF4pw22P1TGVqSsHenRquGJh4MKLiIglsVIRymyFWtqzz4Yt3S3DuFeaBkWvKueA8M4uA6xSg559/Pq666ipcfvnlqF27ZpvAN2vWDOK/8+mnn9aVobIcXqK8t2/fHh06dLDMRxSpVpMoOf/yl7/g/vvvx9q1a7FixQps2bIFRx55JLp27aoHS7LaFuVIgARIgARIgARIgARIgARIgAQyk8AvS8sxb4ZN7VoUFN/PLsPyheU44ricKFKZX3Roh2zIJ9H0xn/22VZ++vv84M1inHxuHuo1YNgYPxNu059A4neVwwxmzpyJ6dOn69aNV155pe6zUpaC1+RUv3593Sen+OWsruT1etGmTRv9U119sh8SIAESIAESIAESIIHqIeD75HVoCz+z1Zm2doUu7/vkNWiLZ9mq6zmlN7w9r7BVh8IkQALuJSBxIcY+53zAnPEjCvHwS/XhzUrP5dduOWNrV1VgzrT4TXOLCzW892oRrrm3rlumxHGQQMIEXKcA9c9IopzL0m/5iLXj9ddfj2uvvRYtWrTwi3BLAiRAAiRAAiRAAiRAAiQQBwHf1JeAn7+Po6aqouppduvu2wlQARofb9YiARcSmPtJKdasVGHDHU4bfq3Elx+W4oze+Q63XLOaG6cUyftjF8c98a+mqvNwST5aHe5atVHcc2PFmknAdVeyLPl+8803IZagPl9V1DNZ+j148GDdH+Z5552nW4X27t1bXw5eM08bZ00CJEACJEACJEACJEAC8RPIevwDYP1KWw34ZoyBNm0UPJcNgveUS6zX9ShLrtZHWJenJAmQgKsJlCjfkpNfLkraGN97rQjdzspFnXpcfh0P5PlflGLV4op4qobUEQXquOGF+PPQ+iH5PCCBdCXgOgXoddddB/msW7cOY8aMwVtvvYVly5bpfEUh+vHHH+ufhg0bon///roy9IQTTkhX/hw3CZAACZAACZAACZAACVQ7AU+BCqKpIuXaSZ7FX0J9H4aneVtIlF0mEiCBmklg9rQSZOd40KhZ8papixXoBf0ZhMfuFVZWqmGiCkzlVFqpFKkLZpXihJ55TjXJdkggZQRcpwD1k2jVqhUefPBB/bNw4ULdKnTs2LHYunWrLrJz5048//zz+kcC9Nxwww24+uqrIYGDmEiABEiABEiABEiABEiABEiABEiABJwncM4fa0E+TO4jMH18MXZsqVpJ69ToJr5QhKO75yInN3kKb6fGynZIIBqBtLApP+644zB06FBs2LABU6dORb9+/VCr1oEH7pIlS3Dffffp/kH79OmDKVOmRJszy0iABEiABEiABEiABEiABEiABEggJQS2b6mEfJhIwEkCO7ZVYtrYYieb1NvarhSqnyjFKhMJpDuBtFCA+iFnZ2fjwgsvhFiCbt68GW+88QYuvvjigC/QiooKXfkpSlAmEiABEiABEiABEiABEiABEiABEnAbgYkvFkE+TCTgJIFJaul7WfyB36MORRSrO5WClYkE0plAWilAg0EXFBToUeFfe+01DBkyBHLMRAIkQAIkQAIkQAIkQAIkQAIkQAJuJbDqx3IsmFmmf2SfiQScIPDzknLMm1HmRFOGbZSVAJOSGPjKsFNmkoDDBFzrAzTaPLdv34533nkHEydOxJdffgmx/AxOhxxySPAh90mABEiABEiABEiABEiABEiABEggpQR8Pk2Pqu0fhETY/tvI+vB66VvRz4Tb+Ai0apeN/05uGF9lq7V4mVolRTmXEkgbBWhJSQnef/99PTL8tGnTUF4e+mtZbm6uvhz+xhtvxPnnn+9S3BwWCZAACZAACZAACZAACZAACZBATSQwZ1op1q46sIxY9iWvx0X5NREH5+wggbxaHsiHiQRIwJyAqxWgPp8PM2fOxOjRozFp0iTs2bMnYiZHH300ROl51VVXoXHjxhHlzCABEiABEiABEiABEiABEnCOgKZpzjXGlkighhAoLvTh3Vcj/X5K3gln5KJWnbT1TldDziCnSQIkkO4EXKkAXbRoka70lGBHEvk9PDVq1AgDBgzQFZ/HHntseDGPSYAESIAESIAESIAESIAEkkTgW+VnrtNJlWjUNCtJPbBZEkgOgUofkJUiPeOHbxVj787IHw8kT8quGFgnOZNmqyRAAiRAAjoB1ylARaH5ww8/RJwer9eL8847T1d6XnLJJYHI7xGCzCABEiABEiABEiABEiABEkgagZ+XVGCpimD9fw/VS1ofbJgEnCDw+y5g5kJgwXJg0+/AvmKgVh7QrBFwXEegp7KlaXWQEz1Fb2PL+krMmKyiyJgkKTv9D/k4qCV/VDBBxGwSIAESSJiA6xSgy5YtC5lU+/btccMNN+gR31u0aBFSxgMSIAESIAESIAESIAESIIHqIRBsuzbv8zKc2acchx+ZUz2dsxcSsEGgQrnZHPcp8OFsoDw0Xi6KS4E1m6o+780CzjweuP4PQJ0kuuF85/lCVIaNI3g6UiYyd/67IDib+yRAAiRAAg4SSNECgOgzqFu3rq70/Oqrr7By5Ur89a9/BZWf0ZmxlARIgARIgARIgARIgASSSWDNz2r9sEoaqgJtSARr+gNNJnG2HQ+BfcrN5qMvA+/OjFR+hrcn7mw/XwD8dQSwZUd4qTPHS+eXYfE3oQF8jVoWGZFlIgESIAESSA4B1ylAX3nlFWzevBmvvvoqTjvttOTMmq2SAAmQAAmQAAmQAAmQAAlYJlBSrGHswgsws+J6LK48T6+3ZmUl5n6szOmYSMAlBCqV5efTY4Dlv9kb0IZtwL9eAwrNV6nba3C/dGWlhvHPRwY+MmtMZKUOEwmQAAmQgPMEXKcAveaaa1CnDh1AO3+q2SIJkAAJkAAJkAAJkAAJxEdg6ugi/LarFcZXPI49aBZoZPIrRSgposImAIQ7KSXwrlrSvuSX+IYgStDXPoivrlmtmVNKsGmN0spaTCIrdZhIgARIgAScJ+A6BajzU2SLJEACJEACJEACJEACJEAC8RLYtrESn000VsrsURGsRTnKRAKpJrC3EBCfnokkCZi0dnMiLRyou2+3D++/rqIu2UxSR+oykQAJkAAJOEsgJUGQPv74Y+zcuVOfScuWLdGjR4/ArMaPHw+fL/EHfv/+/QNtcocESIAESIAESIAESIAESCA+Au+MLERFFBeGn00qQY+L8tGsBSNYx0eYtZwg8M1S6AGOEmlLfILOUkrQay5MpJWqulNeK0LRPvvW0VJH6l41qG7ig2ALJEACJEACAQIpUYA++OCDWLRokT6I3r17hyhAr732WpSVJe78mQrQwDnmDgmQAAmQAAmQAAmQAAnERWD5wnL8MCeK9lO1KsrRCUpJevvjjGAdF2RWcoTAwhWONIPvfkpcAbrh1wrM+jB+/7hSt+cl+WjZNiVf150ByVZIgARIwGUEuATeZSeEwyEBEiABEiABEiABEiABNxDwSQCXEWpdsYX0w9xyiLKUiQRSRWBL1QLDhLvf6kA0+HHDC6ElsKhR6lq99xKeMBsgARIggRpCICU/KfXs2ROtW7fWEXfr1i0E9UUXXYSKioqQPB6QAAmQAAmQAAmQAAmQAAlULwGxQtvwq/UALqL0eeTl+vBmeap3oOyNBBSBfQ65oi1Vevxy9XU0J85vygu/KsWK7xP/PittSFvH9cjj+SUBEiABEnCAQJyP9cR6HjZsmGkDkydPNi1jAQmQAAmQAAmQAAmQAAmQQPIJFO716X4I7fS08TcVwfqDUpzVJ99ONcqSgCMECuoA23cn3lR+bvzKz/IyDRNecEgTq6YibXU9KRc5ufxRIfEzyxZIgARqOgEuga/pVwDnTwIkQAIkQAIkQAIkQAJhBCQSdeEe+wFcJHhL4Z4E1v6GjYOHJGCVwCFNrEpGl2ueQDufTSzB75ucu/6lLWmTiQRIgARIIHECVIAmzpAtkAAJkAAJkAAJkAAJkEDGENi0pgIzp8SndCnaqyJYK+UpEwlUN4HjOznT4wlHxNfOru0+TB3tnPWnfxTSprTNRAIkQAIkkBiBlCyBtzpkTdPw66+/om3btiFVJP+tt97CtGnTMGPGDNSuXRtnnnkm/vrXv6JDhw4hsjwgARIgARIgARIgARIgARKwTmDciCL4EtC3zHq/BGdckodD2rj6q4Z1IJRMCwIndgFkGfwea3G7DOeUnQWcebxhUczMyS8XoTS+3w2iti1tSts3Plg3qhwLawaBSuWW+YdVwPxlwLotwM69QC3lJrZJA+Dow4GTuwKNCmoGC86SBOwScOVbyY4dOyB+Qt98800ccsghmDNnTsi8/vGPf0A+wen111/He++9h3nz5qF9+/bBRdwnARIgARIgARIgARIgARKwQGDR3DIsW5BYNHdRnooS9d6n+S3cAnKKOERAlEB9zwVefi/+Bnt1Bw5qZL/+rysq8PX0UvsVLdaQts9UvnUP6+TKr+8WZ5F8MTGUKpqz2pGOPDlZqH1SG0facqqR71YAr38IbPw9ssXfNgELlgNvfARcoK5juRfknmAiARI4QMB1T9CioiKce+65WLhwoT7K4uLQJTRi9fnYY48dmEHQ3q5du9C7d2988803KCjgC1cQGu6SAAmQAAmQAAmQAAmQQFQCFeUa3hmZgPlcUOvLvyvHD3PKcMypKqIMEwlUE4FeJwNLfwHm/mi/w/atgKt72a8nNfbs8KHPTbXiq2yxlvTBFJ2AVlqBX3oMjS5ksTSrUW102T7EonRyxZReF2OnA5O+iN1PhbIQ/WB2lZXo4OuAZnEo9GP3QgkSSE8CrlOAPvfccwHlpyCVX3EqKiqQnV011CeeeELPkzKx9BwxYgTy8vLw9NNP48MPP8Ty5ctxzz33YNSoUSLCRAIkQAIkQAIkQAIkQAIkYIHAjMkl2LrBOSWLKFOPPDEH2TmMYG0BP0UcInDHleo7pGrraxtK0E5tgAeuiT/6+9Gn5EI+TCSQDAITZlhTfgb3Lcvj/6FUIkNuB+rWDi7hPgnUXAKuC4L0xhtv6GcjNzcXf//73/Hzzz8HlJ/r16/H7Nnq54z9SfyAirXo6aefjilTpuDkk9VPfip9/vnn+yW4IQESIAESIAESIAESIAESiEVgz04fPnwrdOVVrDqxyrdtVBGsJykHhkwkUI0E8nKA+wYAN18C1Iuh+BHZK84GHr25yn9oNQ6TXZGAJQJLfgHGf2ZJNEJo83ZgxKSIbGaQQI0l4CoL0PLycqxapTz6qvSnP/0pYqn7Rx99FLD+lGBHJ510UuDEeb1eDBo0CP369cNvv/2GnTt3omHDhoFy7pAACZAACZAACZAACZAACRgTePeVIpQUid2cs2nq6GKccl4eChq5zu7C2YmyNVcR8Cij4wtOAc5QAY2+WVLlG3GT8pu4W3l4qKtWqoufz+M6At1VwJj6jC3kqnPHwYQSEJ+eiaR5S4HlvwFHtEmkFdYlgcwg4CoF6MqVK/Xl7oL20ksvjSAs/j/96YILLvDvBrYdO6r/YvvT999/j7POOst/yC0JkAAJkAAJkAAJkAAJkIABgTUrKzDn4+QEcBGl6uRRRbj+z9QyGaBnVpIJSBAYieoeb2T3JA+PzZNAVAI/rwdWb4gqYqlw+rdUgFoCRaGMJ+Cqn2JXrFBhzfanFi1a+Hf1rViHzpihnF/sT716RXqorlevnr8YGzY48KQItMYdEiABEiABEiABEiABEshMAuOGF6pVVsmb21ylXBUlKxMJkAAJkIB1AhLV3Ykk0eOT+Yx3YoxsgwSqg4CrLEBr1z7gpGX37t0h8587dy727t2r5+Xn56Nnz54h5XKwZs2aQF64AjVQwB0SIAESIAESIAESIAESIIEAgQf+Vz+wzx0SIAESIAF3ENi4zZlxFCr3znuU+4fqdPdQqeLpbdsJ7FQqnJwsoJH6N9OowJn5sBUSiJeAqxSg7dq1C8xDorl369YtcCwR3v2pR48eqFVLOW8JSx9//HEgp3Xr1oF97pAACZAACZAACZAACZAACZAACZAACZBAuhAQpaVTSfzfVocCdO0W4P0vgfnKenVfUejoD24MnKL87l7cg0HHQsnwqLoIuGoJ/GGHHYb69at+gR42bBiKi9VPFSqJZedLL70UYHL55ZcH9v07P/zwA4YPH64fNmnSBIceeqi/iFsSIAESIAESIAESIAESIAESIAESIAESSBsCdSJtvuIee538uKtaqlhRCYx6H7hvKPDFd5HKT2lEotJPngnc9hTw+QJLzVKIBBwl4CoFaE5ODgYOHKhPcOHChboFqESDP+WUU7Bnzx49v06dOujbt28AwtKlS/Vo8aeeempAYXrPPfdA2mIiARIgARIgARIgARIgARIgARIgARIggXQj0KyhMyPOVet+GxwIl+JMo0GtlJUD/xwFfDQX8FnwJ12sYu6NmAiMObCAN6g17pJA8gi4agm8TPPee+/F5MmTsWrVKohyUz7+5PV6MXr06ICVqOTfcccdmDlzpl9Et/yUPCYSIAESIAESIAESIAESIAESIAESIAESSEcCx3VUy8m/SnzkR7UHspJo+ibKzCWr7Y9TrEGbKiXveSfZr8saJBAPgSTeBvEMB2jWrBkk4NHpp58e0oAsjX/uuefQp0+fkPxOnToFjsUKdN68eSgoyBzvuuIG4OWXX8att96K7t27o23btjj77LNx9913Q/ykJiuJUvnGG2+E+FsVf6rHHHMM+vXrhylTpsDnUx6NmUiABEiABEiABEiABEiABEiABEiABJJCoPNhVQrCRBs/8/hEWzCvP0/Zq81eZF4eq+Q1FeplZ9Vi31iiLCeBhAm4zgJUZiQ+PGfNmoUNGzZg9uzZaNmyJU4++WRkZanwYWHphBNOwFVXXYUrrrgCF154YUYtfZ8/fz6uvfZarFixImTWv/76Kz7//HNdISz+UF955RXHlL6///673ue0adNC+ly3bh0WLVqE8ePH66zFEjc3NzdEhgckQAIkQAIkQAIkQAIkQAIkQAIkQAKJExD1x4DzgWHj4m+rfSvg5CPjrx+r5tvTY0lEL5fl85NmAjdfEl2OpSTgBAFXKkD9E2vRokWIv09/fvD2pptugnwyLYmC8YYbbkBFRYU+NY/Hg/bt2+uWmKIAFWVkWVkZJkyYgJUrV0IUls2bN08Ig1ibXnzxxfjmm28C7YiFbdeuXfHdd99h9eoqu3bpU3yyiquC2rVrB2S5QwIkQAIkQAIkQAIkQAIkQAIkQAIk4AyB048Bvv8J+PJ7++1JEKW7D4RPsd9AjBrrVMR3+SSa5i4GbvwDoDweMpFAUgnwEksq3vga37x5M+68886A8rNNmzb49ttv8dNPP+kWmLLM/+eff9aDQ0kPogzt379/fJ0F1ZJl9X7lp7giEAtUWWb/zjvv4JdfftFdEzRu3Fiv8cknn+jL8oOqc5cESIAESIAESIAESIAESIAESIAESMBBArdeBnTrbK/BespO6a/XAc2b2KtnR/rHX+xIm8vu3ges32pezhIScIoAFaBOkXSwnUGDBmHXrl16i2IFK9aX3bp1C+mhVatWupsA8c0pSVwGvP322yEydg7Wrl2L119/Xa8iS9ulPXEvEJzEB6nkiz9WSePGjcPWrXxSBTPiPgmQAAmQAAmQAAmQAAmQAAmQAAk4RSA3B3jgGuCqXkC+BS90Rx0ODFFxoY9o49QIjNvZvts4P55cJ9uKp3/WqRkEXLcEXgIZlZcrRxAJJrGSTMe0fft23crTP/ZnnnkGjRo18h+GbLOzszFs2DD07NlTzx8yZAgGDBgQImP1YOjQoQHuEvwoOLhUcBtdunTRAzA99thj+hJ88T86ePDgYBHukwAJkAAJkAAJkAAJkAAJkAAJkAAJOERAecTDZWcAZysbpRkLAAk+JMvPS8qqlo43VnGgJdp7D2Uf1bWdQ53GaKY0cbVNoAcn2wo0yh0SCCPgOgXoggULdMVa2DhrzOGyZcsCcxWfnn37Rnfacfrpp+OQQw7Bxo0b8eOPP+oWmbJ83W6SJe3+JAGloiWJBi8KUEkvvPACHnjgAcMAVdHaYBkJkAAJkAAJkAAJkAAJkAAJkAAJkIB1AvXrVilCRRkqqVQpQHOUVicV/jMb1qsagxN/nWzLifGwjcwk4DoFaCKYJSBPXl5eIk2kvG6wArRz586WxtOhQwddAappGmbMmGHbH+ju3bt1X5/SmSx/FyvcaOmII45A06ZNsW3bNkh0eAnCJHlMJEACJEACJEACJEACJEACJEACJJAKAp4sL+qe18mRrrMK8h1pJ9mN5FlYEp+sMbRJLAZzYFjqtKGlfRuuQH3ukIBVAq5TgE6fPh0+ny/q+EXRt2/fPuzYsUMP1CNBen7//Xccfvjhuo/KBg0aRK3v5kLxxelPEvzISmrS5IBnY7ECtRsQSYIdCVNJ0qcVJbJEpBcFqCRR2lIBqqPgHxIgARIgARIgARIgARIgARIggRQQ8ORkoe0nt6eg55rZpSy1F5+ksgw/kdSlLSAR65lIINkEXKcA9fuztDrx66+/Hvfeey969eqFxYsX4w9/+AM+/fRT1KqVnnfQYYcdFpi6WFdaSatXrw6I+YMnBTIs7Kxfvz4gZVXp2rp1az0qvFRctWpVoD53SIAESIAESIAESIAESIAESIAESIAEMpuABGfq1R14b1Zi87ykR2L1WZsErBJQxsbpn9q1a6cr48T35Zw5c3DXXXel7aSCLSlFoRvLGrasrCxEAblz507bc9+zZ0+gTuPGjQP70XaCAzMF1xdL0ng+0fpiGQmQAAmQAAmQAAmQAAmQAAmQAAmQQOoJFO07sGJXfJE2UgGY4k3HKY8Fx3aMtzbrkYA9AhmhAJUpi0/K888/X5/91KlT7VFwkbT4/fTu92C8efNmPP/881FHJ9Hb9+7dG5CJxwJUfID6k1XL2fz8Az5RCgsL9erjxo3Txy7jt/uRCPZMJEACJEACJEACJEACJEACJEACJEAC7iTw85JyvPyvfYHBydL1B65VsUTiWFvcXHnyGxQ95nOgH+6QgBME4rhMneg2OW2cd955eOutt7Bp0yb88ssvEMvQdEsNGzbE7bffjueee04f+uDBg9G1a1cYuQYQa9fHH388ZIoVFf/P3n3AV1GlDRx+Q5MOIlWaICBdUIqACoIioCiKIDZQ1oJgxbUhq+63FkTFgi6CuBRBRaTYsCNFQRaldxCkiRSlh9Byv3mPO+Pc5NZkktzyPz/jnTtz5sw5z9xJyJtTTvi9j+SNuwdnVgKgqamp5jKVK1c2UxBEcs2MebQnq3sof8bjvEcAAQQQQAABBBBAAAEEEEAAgbwRSE/3yXuvHZbN607K8h+OSaPz/lyBqVYVkX/eLvLc2yL7/uqbFbKSdapZgdObmPszJBIHPRdIqABoSkqKA6QL+8RjAFQboEHNDz74wARytXdn+/btpV+/fmaeU13xXYO7Os+pBklPnjwp7dq1k1mzZpm2Z2UBqIIFrck7/pciDaDqde1kL5p0wQUXiH5lJY0aNUp0ASwSAggggABilWOSAABAAElEQVQCCCCAAAIIIIAAAgjElsD3nx81wU+t1aQRh6Ves4JSoMCfMRgNaL50n8j734h8tUDkxF/hAr9GlCgqck37P+cOLZDf7xBvEMhxgYQKgH7yyScOmC7SE6+pZMmSMnHiRLn22mvNSus6D6gOhQ80HF6Hjmtb7QBoqVKlom528eLFnXPS0tKc7VAb7nxZuWaosjmGAAIIIIAAAggggAACCCCAAAKxIZCW6pNpb/058lNrtHNrunw7LU0u6fHX4tMli4nceoXIdR1Fflojsmm7yB/WciMa6CxjhSkaWOs9N7QG6RL4jI17moy1SJgA6Oeffy723J/58+eXJk2axPX9vOiii2T58uVyyy23yGeffZapLdrTU4Oft99+u3m1M2SlB6g7AGoPZ7fLC/Zqz/upxzVgS0IAAQQQQAABBBBAAAEEEEAAgcQT+OTtVDm41+fXsI/HH5HzLjlFSpT2X1qmmLVcyIVWOEa/SAjEkkDMBUBnzpxpVhEPh6RDsHXeyD/++EM0+Pnuu+86pzRv3lyKFrX6Vsd5qlChgsyYMcMMeV+0aJEsWbJEChUqJHXr1pWuXbs6bdy8ebPT0ooVKzrbkW7o3J122r7d+jNNBOnXX391cum8pSQEEEAAAQQQQAABBBBAAAEEEEgsgV3bT8rXUzKPFD1y2CfT/5MqNw38a0RpYrWc1iSaQMwFQDt37mwCm1mF1sDn6NGjs3p6TJ6nc5nqV48ePQLWb8OGDc7+1q1bO9uRbujK83basmWLvRny1R101UWaSAgggAACCCCAAAIIIIAAAgggkFgCk/59WE4GWWt57qdHpd0VhaVqrZgLLSXWTaA1ngj491X2pMi8K0SHf2vws0GDBnlXiVy+8u7du535PwsXLiznnntu1DWoXbu22AsZbdu2TXbu3BmyDF2Yae3atSaPLqDUtGnTkPk5iAACCCCAAAIIIIAAAggggAAC8SWw6sdjsmz+8aCV9lmj4nVleBIC8SAQc2H6Fi1ayPHjwR8wN6rO9amBu/Lly8vFF18s119/vTMs3J0vnra17W3bthUNROo8mzrU3A5OBmrH+PHjHa9WrVqFzBvofN1XoEAB42fPofr111/LDTfcECy7Cbjaq8Vr8FMDryQEEEAAAQQQQAABBBBAAAEEEEgMgZMnffLe638tfBSsVeuWnZAfZx+VZm1PCZaF/QjEhEDMBUDnzp0bEzB5VQntUanzfG7dutVUQYORl112WcDqHDp0SEaOHOkce/DBB53taDd69uzpLCI1bNgwE0xOSUkJWMyLL77o7P/b3/7mbLOBAAIIIIAAAggggAACCCCAAALxLzDrwzTZsflkRA354I1UObtVISlYKHAMIaJCyIRADgsk1BD4HLbKteK7dOniXGvgwIGmJ6iz438bOgy9U6dOsn79erNH5/7U+VMDpVGjRsmQIUPMly6qFCjp/KLVq1c3h3TBpb59+4ouNOVOPqt/+1133SWzZ882uytVqiR9+vRxZ2EbAQQQQAABBBBAAAEEEEAAAQTiWODwgXT5aNyRiFvw+850+WJS5PkjLpiMCHgoEHM9QD1sW9wWNWDAABk3bpysWrVK1q1bZ+b1fOKJJ8zrnj175JtvvpFJkybJypUrTRvLlSsnb775ZtD2ao9NLUfTLbfcIu4Aq31SkSJF5JVXXpFu3bqZXWPHjjXD7zXA2bx5c1m8eLFMnDhRPvroI3Nce4e++uqrWRpyb1+TVwQQQAABBBBAAAEEEEAAAQQQiC2BD8ekSupBa4LPKNJn7x6RNp1OkVPL5Y/iLLIikHsCBEBzzzriKxUrVkwmT54sbdq0kX379pkFh3R+00CpbNmyJiDqXsk9UL5I9l155ZXy/vvvmyCpzj/65Zdfmq9A577wwgtyzTXXBDrEPgQQQAABBBBAAAEEEEAAAQQQiEOB7ZtOyKyPj0Zd82NpIlPeTJVbB5WI+lxOQCA3BPIkALpgwQLR+StzMnXo0CEni8/xsjWgqSutDx48WN566y1JT0/3u2aJEiXMcPQHHnhATjvtNL9j2XmjQ+H12jq3508//ST2Ykd2mdobdOjQodKuXTt7F68IIIAAAggggAACCCCAAAIIIJAAApOshY98/uGHiFu14OtjclG343Jm/YIRn0NGBHJLIE8CoHfccYcsXbo0R9uo81XGe9LV7XX+zueff94EQ3UYu64IX7t2balTp07EK95rIDWa1KBBA/nhhx8kLS3N3KfVq1eLzvep161Ro4YEWxwpmmuQFwEEEEAAAQQQQAABBBBAAAEEYkdg8XfHZPWi49mq0Huvpcqg10sSN8iWIifnhECeBEBzoiGJXGapUqWkRYsW5is321m4cGFp2bKl+crN63ItBBBAAAEEEEAAAQQQQAABBBDIPYHjx3wy+Y3D2b7gL2tOyPwvj0rrSwtnuywKQMBLgTwJgDZq1Eg0uEZCAAEEEEAAAQQQQAABBBBAAAEEEMhbga+npMnuX7M49j1D1adac4Gec+EpUrhISoYjvEUg7wTyJAD69ttv512LuTICCCCAAAIIIIAAAggggAACCCCAgBHY/0e6fDoh1TON/X/4ZMbEI3L1rUU9K5OCEMiuQL7sFsD5CCCAAAIIIIAAAggggAACCCCAAALxKaA9No8e8bbuX00+Irt3nPS2UEpDIBsCMR0A/e233+SVV16ROXPmBGzit99+a+bFfPbZZ2XDhg0B87ATAQQQQAABBBBAAAEEEEAAAQQQQCCzwC9rT8i8L45mPpDNPSestZQmv+Fdr9JsVofTEZCYDIDqCu6PPvqoVKlSRe677z6ZNWtWwFv1888/y8KFC2XQoEGiK5ePHDkyYD52IoAAAggggAACCCCAAAIIIIAAAgj4C7z3WvYXPvIv8a93i+cekzWLs7eq/F+lsYVA9gTyZA7QUFXW4Oe1114rkydPdrKtXbvW2XZvbNq0yXl77Ngx6devn6xevVpefvllZz8bCCCAAAIIIIAAAggggAACCCCAAAL+AuuXHxdd/b1a7fz+Bzx8pyvC121a0MMSKQqBrAnEXABUA5/u4Gfnzp3lpptuCti6wYMHy4UXXijvvvuujBs3zuTRIfPdu3eXCy64IOA57EQAAQQQQAABBBBAAAEEEEAAAQSSXaB2o4Lyj5Glk52B9ieJQEwNgdfen48//rihz5cvn4wfP15mzJghnTp1Cng7ihQpIpdeeqmMHTtWpk6dKnqOpnvvvTdgfnYigAACCCCAAAIIIIAAAggggAACCCCAQHIJxFQAdPv27WIPd7/tttuC9vwMdIuuuuoq0XM0LV68WHbt2hUoG/sQQAABBBBAAAEEEEAAAQQQQAABBBBAIIkEYioA6l7JXecBjTZdc801zik6FygJAQQQQAABBBBAAAEEEEAAAQQQQAABBJJbIKYCoDt27HDuRrly5ZztSDeqV6/uZN26dauzzQYCCCCAAAIIIIAAAggggAACCCCAAAIIJKdATAVAK1as6NyFZcuWOduRbqxZs8bJWqtWLWebDQQQQAABBBBAAAEEEEAAAQQQQAABBBBIToGYCoCeeeaZzl3QxY+iTV9//bU5JSUlRRo1ahTt6eRHAAEEEEAAAQQQQAABBBBAAAEEEEAAgQQTiKkAaLVq1aRFixaGeOLEiTJp0qSIuTX4+dprr5n8derUkWLFikV8LhkRQAABBBBAAAEEEEAAAQQQQAABBBBAIDEFYioAqsT9+/d3pG+++WYZOHCg7Ny509mXcWPv3r3y5JNPSvfu3SU9Pd0cfvbZZzNm4z0CCCCAAAIIIIAAAggggAACCCCAAAIIJKFAgVhrc+/evWX69OnmKy0tTV566SUZOXKknH322aI9RKtWrSo6xF0XOdq2bZssWbJEDh065DRDg6ZXXXWV854NBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhegZgLgGpwc9y4caZHpz2nZ2pqqsyfP998hbpV3bp1k1dffTVUFo4hgAACCCCAAAIIIIAAAggggAACCCCAQBIJxNwQeLUvWbKkfPXVVzJ27FipV69e2NvRuHFj+eabb2TatGlSokSJsPnJgAACCCCAAAIIIIAAAggggAACCCCAAALJIRBzPUDd7H369BH9Wr9+vcycOVN+/fVXMx9o/vz5RVeMr1Wrlvk666yzRPeREEAAAQQQQAABBBBAAAEEEEAAAQQQQAABt0BMB0DtitauXVv0i4QAAggggAACCCCAAAIIIIAAAggggAACCEQjEJND4KNpQKC8Pp8v0G72IYAAAggggAACCCCAAAIIIIAAAggggECSCcRcAHT27NnZugU7duyQTp06ZasMTkYAAQQQQAABBBBAAAEEEEAAAQQQQACBxBCIuQCoBi8fe+wxOXHiRNTCU6dOlUaNGsmXX34Z9bmcgAACCCCAAAIIIIAAAggggAACCCCAAAKJJxBzAdD09HR55plnpG3btrJly5aIxA8ePCh9+/aV7t27y++//x7ROWRCAAEEEEAAAQQQQAABBBBAAAEEEEAAgcQXiLkAqE0+b948adKkiWivzlDJzjdmzBgnW/ny5Z1tNhBAAAEEEEAAAQQQQAABBBBAAAEEEEAgeQViLgA6atQoKVGihLkje/fuNb06BwwYIGlpaX53SYfIP/7443LhhRfKxo0bnWPXXXedrFq1ynnPBgIIIIAAAggggAACCCCAAAIIIIAAAggkr0DMBUD79OkjixcvlpYtWzp35d///rd5v3r1arNv/fr10rp1a/nXv/4lJ0+eNPsqVqwo06ZNk3feeUdOO+0051w2EEAAAQQQQAABBBBAAAEEEEAAAQQQQCB5BWIuAKq34swzz5TvvvtOBg8eLPnz5zd3Z9myZdKsWTO55557pGnTprJw4ULnrt10002m12e3bt2cfWwggAACCCCAAAIIIIAAAggggAACCCCAAAIxGQDV21KgQAHTw3P27NlSs2ZNc6dSU1Nl+PDhcvjwYfO+SpUq8sknn8j48ePl1FNPNfv4HwIIIIAAAggggAACCCCAAAIIIIAAAgggYAvEbADUrmCbNm3M0PaiRYvau8xrSkqKDBs2TC677DK//bxBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRsgZgOgOpCR88++6ycd955or0/3cnn80nPnj3ltttukwMHDrgPsY0AAggggAACCCCAAAIIIIAAAggggAACCBiBmA2A6kJIzZs3l0GDBsmRI0dMZStUqCBjx46VCy64wLl9o0ePloYNG8oXX3zh7GMDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBQgZgLgB49elQee+wxadGihSxZssS5S1dccYUsX75cdJX4WbNmyTPPPCMFCxY0x7du3SqdOnWiN6ijxQYCCCCAAAIIIIAAAggggAACCCCAAAIIqEDMBUC116cGN3X4u6ZixYrJyJEj5cMPP5Ry5cqZffny5ZNHH31UfvjhB6lbt67Zp/+jN6hDwQYCCCCAAAIIIIAAAggggAACCCCAAAIIWAIxFwBdu3atc2M0GKpD4W+//XZnn3vjnHPOkUWLFkn//v2d3XZvUGcHGwgggAACCCCAAAIIIIAAAggggAACCCCQtAIxFwDVO5E/f375xz/+IfPmzZPatWuHvDlFihSR119/XWbMmCE6RygJAQQQQAABBBBAAAEEEEAAAQQQQAABBBCwBQrYG7HyWrNmTXnrrbekdevWUVWpc+fOZo5QXRVeh8snStLV7t977z2ZPXu2rFmzRn755RepVq2a1KtXzxjddNNNolMCeJWmT58uq1atirg4vV+9evWKOD8ZEUAAAQQQQAABBBBAAAEEEEAAAQQQyE2BmAuA/vjjj2bez6wg6ByhGsDTuUATIemiT3feead8//33fs3ZvHmzzJ07V0aNGiWvvfaavPHGG3Luuef65cnqm6eeekp++umniE/v2LEjAdCItciIAAIIIIAAAggggAACCCCAAAIIIJDbAjEXANVFj7Kbbr311uwWkefnb9myRS688ELZt2+fqUuBAgVE50StVauWaAD0v//9r6SlpYkGjM8//3zz2qBBg2zVWxeeWrFiRbbK4GQEEEAAAQQQQAABBBBAAAEEEEAAAQRiSSDmAqDZwdFekcePHzdFtG/fPjtF5em56enp0rt3byf4qcPd33nnHWnSpIlTrw0bNsgNN9zgBEKvv/56s33KKac4eaLdWL16tRw9etScpkPb77nnnrBFVK9ePWweMiCAAAIIIIAAAggggAACCCCAAAIIIJBXAnkSAB04cKBs2rTJtFmHq5922mmetL9bt27yxx9/mLJ07sx4TToEXef81FS0aFEzrL9OnTp+zdGeoDrcv2HDhqbNy5Ytk48//liuueYav3zRvFmyZImTXQPI9957r/OeDQQQQAABBBBAAAEEEEAAAQQQQAABBOJRwLvVc6Jo/cyZM03wTgN4qampQc9csGCBtG3b1nwNGTIkaL5EO7Bo0SKnSRdffLFkDH7aBytVqiTXXnut/TaquTudk1wb7gCoV3OKuopnEwEEEEAAAQQQQAABBBBAAAEEEEAAgVwXyJMeoJG2Untzzpkzx2SvUaNGpKfFfb61a9c6bTjjjDOc7UAb7uDoypUrA2WJeN/ixYudvM2aNXO22UAAAQQQQAABBBBAAAEEEEAAAQQQQCBeBfKkB2i8YuVWvbVnp53WrVtnbwZ83bhxo7O/fPnyznZWNuweoAULFpRGjRplpQjOQQABBBBAAAEEEEAAAQQQQAABBBBAIKYEYroHaExJ5WJlOnTo4FxNpwvQldl1rs+Mac+ePTJp0iRnd3YWftJV5/fu3WvK0mvpYkq6oJQujKRfhw8flsaNG5t6FC5c2LkmGwgggAACCCCAAAIIIIAAAggggAACCMSyAD1AY/DunHPOOdKzZ09Ts2PHjkn37t1l4cKFfjXVnp9XXnml7Nq1y+zXFeLd84H6ZY7gjd37U7Nq788RI0aITjtw9tlnS69eveRvf/ubNG/eXEqUKGHea/CVhAACCCCAAAIIIIAAAggggAACCCCAQKwL0AM0Ru/Q+PHjJSUlxfTw1GHw5513numBqau/b9u2TXS+zqNHj5ra6zFdAT5//vxZbo17/s8JEyaIXj9QOnHihKnTrFmz5D//+Y906dIlUDb2IYAAAggggAACCCCAAAIIIIAAAgggEBMCBEBj4jZkroQOQX/vvffkwgsvlLvuukvS09NFe2m6e2rqWQMGDJDhw4ebYGnmUiLf4y5Xr1WsWDFp166d+apfv75oEPa7776TKVOmmEJ37txpeqYuXbrUWaVej7/wwguRX9SVMy0tzfWOTQQQQAABBBBAAAEEEEAAAQQQQAABBLwRIADqjaPnpWzatEl69+5tgo524dojVBc60mHvPp/P7H799ddNb9BRo0ZJgwYN7KxRv7oDoKeffrp8+eWXfuVpT8/77rtPPv/8c1Ov3bt3iwYtb731Vpk9e7YJwGrP1A8//DDqa+sJNWvWzNJ5nIQAAggggAACCCCAAAIIIIAAAggggEAoAeYADaWTR8d00aE2bdo4wU+di1OHuO/fv19+++03OXjwoAlQas9MTfPmzTM9NbU3ZlbT0KFDZciQIdK/f39z3WDB1E6dOpkep/Z15s6dK9OmTTNvdQ5SHSKfla8HH3zQLpJXBBBAAAEEEEAAAQQQQAABBBBAAAEEPBOgB6hnlN4VdPfdd8uOHTtMgc2aNZNvv/1Wihcv7lxAh6dfcsklpuenLpD0ySefiC5KdMMNN8jy5cuzNBy+R48eTvnhNjTQ+corr8j8+fNNVl2g6eqrrzbXzeo8pPnyEYsP585xBBBAAAEEEEAAAQQQQAABBBBAAIHoBYg6RW+Wo2doMPGbb74x1yhYsKCMHTvWL/jpvnihQoVk9OjRctppp5ndK1eulOnTp7uz5Nh2q1atnLJXrFjhbLOBAAIIIIAAAggggAACCCCAAAIIIIBALAnQAzSW7oZVlx9//NGp0bnnnus3D6dzwLVRoUIFueKKK2TMmDFm75w5c+Sqq65y5ciZzbPOOsspWBdIIiGAAAIIIIAAAggggAACCCAQCwInT4os/1lk4WqR7btE9h8WKVpYpPypIk1qizSzZpMrZr0nIYBA8ggQAI2xe71x40anRo0aNXK2Q200btzYOfzzz9Z3+VxIqampzlV00SQSAggggAACCCCAAAIIIIAAAnktsHCVyPgZIr/uyVyTNb+IzFn8ZzD06nYiXS8QKZA/cz72IIBA4gnkeQD0+PHjcuzYsYCyesxO6enpQfPZeeyV0e338fhatmxZp9q64FEkye1UsmTJSE7xyzNz5kz54IMPzOryVatWlZdeesnveKA3a9ascXbXrVvX2WYDAQQQQAABBBBAAAEEEEAAgdwWsEIGMuFzkQ/nhL9yatqfeRdbgxkfvEGkRLHw55ADAQTiWyDPA6BnnnlmRIJvv/226FeipyZNmjhN1BXWNfAbboGgZcuWOec0bdrU2Y50Iy0tTUaMGGGyFy5cWJ555hkpUqRI0NNPWuMJtG52CrZivH2cVwQQQAABBBBAAAEEEEAAAQRyUmBihMFPdx1WWgMwnx4r8q87RArmeXTEXTO2EUDAawEWQfJaNJvlaQAzJSXFlLJv3z7TMzNUkRs2bJCpU6c6WXTe0GhT27ZtRRdU0qTB0HHjxoUsYuTIkbJqlTWuwEraY7V3794h83MQAQQQQAABBBBAAAEEEEAAgZwS0GHv0yPo+Rno+uu3ioz7NNAR9iGAQCIJEACNsbtZvnx5uf/++51a3XTTTfLZZ585790bO3fulBtvvFHs+Tg7duwoGszMmEaNGiVDhgwxXzNmWJOhZEjFihWTa6+91tn7wAMPyNKlS5337o0vvvhCBg0a5Ox6/PHHJSvD7p0C2EAAAQQQQAABBBBAAAEEEEAgiwInraHvbwf+lTniEr9YYM0Zujvi7GREAIE4FMiTTt5vvvmmHDp0KA65cqfKOgRdA40rV640855effXVct1118lFF10krVq1Ep0bdMGCBWao+h9//GEqVaZMGbMSvN171F3TF198UeyV2m+55Rbp0qWL+7DZfv31102Zmk8Dqi1atJCBAweavFWqVJEVK1aIBk+196c912qHDh2kX79+mcpiBwIIIIAAAggggAACCCCAAAK5IbDCWgd4ezaDlzp/6Ff/FelzWW7UmGsggEBeCORJALR58+Z50da4ueYpp5wi06dPlz59+si8efPMsPQxY8aYAGegRtSuXdscy85q7CVKlDBD6bt16yY6rF4XprJ7jQa6pgZHhw4dKvnz5w90mH0IIIAAAggggAACCCCAAAII5LjAwtXeXELLIQDqjSWlIBCLAgyBj8W7YtWpVq1a8t1335kel5UqVQpYSx16rkPQly9fLm3atAmYJ5qdupiR9jp97rnnpFSpUplO1XlCmzVrJhMnThTtVUrwMxMROxBAAAEEEEAAAQQQQAABBHJRYPsuby722+8iOpyehAACiSmQJz1AE5PS+1bpcPbbb7/dfO3Zs8csPPTzzz+LDkmvV6+eeY3kqmvXro0km8mjQc6HHnpI/v73v8uWLVtkzZo1onONNmzYUBo1auQslhRxgWREAAEEEEAAAQQQQAABBBBAIIcE9h/2pmCfT+SgVVbpEt6URykIIBBbAgRAY+t+BK2NrrZ+4YUXmq+gmTw8kC9fPjnjjDPMl4fFUhQCCCCAAAIIIIAAAggggAACngkUK+xZUVLUw7K8qxUlIYCAFwIMgfdCkTIQQAABBBBAAAEEEEAAAQQQQCDXBcqd6s0lSxUXKVTQm7IoBQEEYk+AAGjs3RNqhAACCCCAAAIIIIAAAggggAACEQg0qRNBpgiyNPWonAguRRYEEMgDAQKgeYDOJRFAAAEEEEAAAQQQQAABBBBAIPsCzeqKFCuS/XLanpP9MigBAQRiV4AAaOzeG2qGAAIIIIAAAggggAACCCCAAAIhBHTezqvbhcgQwaHGtUT0i4QAAokrQAA0ce8tLUMAAQQQQAABBBBAAAEEEEAg4QW6ni/S6MysNbNkMZH+12TtXM5CAIH4ESAAGj/3ipoigAACCCCAAAIIIIAAAggggEAGgfz5RR64QaROtQwHwrzV4OdjN4uUKx0mI4cRQCDuBQiAxv0tpAEIIIAAAggggAACCCCAAAIIJLdAiaIi/3e7yGVtRPJHEOnQIe9D7xapVTW53Wg9AskiUCBZGko7EUAAAQQQQAABBBBAAAEEEEAgcQUKWhGOvl1FOrcS+fq/IgtXi+zYI5Lu+7PNpYuLND1LpG1Ta8g8c34m7geBliEQQIAAaAAUdiGAAAIIIIAAAggggAACCCCAQHwKVCorclOXP7/S00UOporoYkkaICUhgEByCvD4J+d9p9UIIIAAAggggAACCCCAAAIIJLxAPms4fCmr5ycJAQSSW4AAaHLff1qPAAIIIIAAAggggAACCCCAAAIIJJXA0WMie/aLHDgsovPHlrUWwipcKKkIkq6xBECT7pbTYAQQQAABBBBAAAEEEEAAAQQQQCD5BOYvt+aHXSiy4meREyf/ar/2FK5fQ6RDM5HzzxbR96TEEiAAmlj3k9YggAACCCCAAAIIIIAAAggggAACCLgEdu0VeeldkXVbXDtdmzpXrAZF9evDOSIDrxepXM6Vgc24FyCmHfe3kAYggAACCCCAAAIIIIAAAggggAACCAQS2PSryMOvBQ9+Zjznlx0ij7wusvqXjEd4H88CBEDj+e5RdwQQQAABBBBAAAEEEEAAAQQQQACBgAL7Doo8M/bPuT4DZgiyMzVN5LnxIrv+CJKB3XEnQAA07m4ZFUYAAQQQQAABBBBAAAEEEEAAAQQQCCcw9lORPw6EyxX4+MFUkTc/DHyMvfEnQAA0/u4ZNUYAAQQQQAABBBBAAAEEEEAAAQQQCCGwdafI3CUhMkRwaNFakTWbI8hIlpgXIAAa87eICiKAAAIIIIAAAggggAACCCCAAAIIRCMwb1k0uYPn/W5p8GMciR8BAqDxc6+oKQIIIIAAAggggAACCCCAAAIIIIBABALLrBXdvUgrNnhRCmXktQAB0Ly+A1wfAQQQQAABBBBAAAEEEEAAAQQQQMBTgd/3e1PcHo/K8aY2lJJVAQKgWZXjPAQQQAABBBBAAAEEEEAAAQQQQACBmBQ4esybah077k05lJK3AgRA89afqyOAAAIIIIAAAggggAACCCCAAAIIeCxwaglvCiztUTne1IZSsipAADSrcpyHAAIIIIAAAggggAACCCCAAAIIIBCTAtUreVMtr8rxpjaUklUBAqBZleM8BBBAAAEEEEAAAQQQQAABBBBAAIGYFGhR35tqeVWON7WhlKwKEADNqhznIYAAAggggAACCCCAAAIIIIAAAgjEpEBzKwBaoUz2qla6uMgFTbJXBmfHhgAB0Ni4D9QCAQQQQAABBBBAAAEEEEAAAQQQQMAjgQL5RW6+LHuF3dBJpHCh7JXB2bEhQAA0Nu4DtUAAAQQQQAABBBBAAAEEEEAAAQQQ8FCgRQORqy/KWoGXnifSvlnWzuWs2BMoEHtVokYIIIAAAggggAACCCCAAAIIIIAAAghkX+CGS61enAVF3vky8rK6tRXR80iJI0AANHHuJS1BAAEEEEAAAQQQQAABBBBAAAEEEMgg0L29SIMzRd6eIbJmc4aDrrc1Thfp3UWkcS3XTjYTQiDFZ6WEaAmNiGuBUaNGyR133CEFCxaU/PmtiTpICCCAAAIIIIAAAggggAACCCCAgMcCKfkKSb4CxSQln9UtNMWKP/jSrf+OSfqJw+bV48tRnEvg6NGjUqxYMTl48KBrb+5s0gM0d5y5ShiBOnXqSN26dcPk4nA8COjfVPbv32+C2fqNjYQAAvEvoM90vnz5pESJEvHfGFqAAALml4709HQpVaoUGgggkAAChw8fluPHj0vp0qUToDU0AYG8EihiXTg2fi6mpqbKsWPHpGTJkubf4HklklPXrVGjRk4VHbJceoCG5OEgAghEK5CWliZFihSR7t27ywcffBDt6eRHAIEYFChXrpzUrFlTFixYEIO1o0oIIBCtQNOmTWXXrl2yffv2aE8lPwIIxKBA165d5ZNPPhH9w0ZKSkoM1pAqIYBANAL9+/eXESNGyObNm6VatWrRnEreEAKsAh8Ch0MIIIAAAggggAACCCCAAAIIIIAAAgggEN8CBEDj+/5RewQQQAABBBBAAAEEEEAAAQQQQAABBBAIIUAANAQOhxBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvgUIgMb3/aP2CCCAAAIIIIAAAggggAACCCCAAAIIIBBCgABoCBwOIYAAAggggAACCCCAAAIIIIAAAggggEB8CxAAje/7R+0RQAABBBBAAAEEEEAAAQQQQAABBBBAIIQAAdAQOBxCAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgWIAAa3/eP2iOAAAIIIIAAAggggAACCCCAAAIIIIBACAECoCFwOIQAAggggAACCCCAAAIIIIAAAggggAAC8S1AADS+7x+1RwABBBBAAAEEEEAAAQQQQAABBBBAAIEQAgRAQ+BwCAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiW4AAaHzfP2qPAAIIIIAAAggggAACCCCAAAIIIIAAAiEECICGwOEQAggggAACCCCAAAIIIIAAAggggAACCMS3QIH4rj61RwCBWBPInz+/XHrppdKkSZNYqxr1QQCBLAq0b99eKlasmMWzOQ0BBGJNoHXr1rJv375Yqxb1QQCBLAqcc845cvz4cUlJScliCZyGAAKxJFC/fn3zO3WRIkViqVpxX5cUn5XivhU0AAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCCDAEPgAKOxCAAEEEEAAAQQQQAABBBBAAAEEEEAAgcQQIACaGPeRViCAAAIIIIAAAggggAACCCCAAAIIIIBAAAECoAFQ2IUAAggggAACCCCAAAIIIIAAAggggAACiSHAIkiJcR9pBQI5JjB16lTp16+flC1bVlatWhX0OsuXL5dp06YFPR7uQO3ateW6664LmG369Okhr53xpJo1a0qvXr0y7uY9AghYApE+0xmxcuI5nDVrlowfP17Wr18vmzdvljJlykjdunXN94KuXbtKvnz8nTbjfeA9AhkFsvpMazlHjx6VsWPHyqJFi8xzuGnTJilXrpx5DvVZvOWWW6RSpUoZL5npfU58f8h0EXYgkEQC2XmuvfzZ6mVZSXT7aCoCmQT4XTkTSd7s0EWQSAgggEAggV27dvkqVKigC6X5rF+IAmVx9k2YMMHk07xZ+brsssucsjJunHvuuVGV2bFjx4xF8B4BBCyBaJ7pjGBePoe7d+/2de7cOeRz3aNHD58VnMlYDd4jgIBLIDvP9Pvvv++rUaNGyOewRIkSvmHDhvms1aVdV8286eX3h8ylsweB5BLI6nPt5c9WL8tKrrtHaxEILMDvyoFdcnsvXSvyJu7MVRGIeYG9e/fKJZdcIjt37syVuqakpAS8zokTJ2TFihUBj7ETAQQiF8jOM+3lc3jkyBHR3p2fffaZU3ntaWYFPEV7b9tp8uTJcsUVV0hqaqq9i1cEEHAJZOeZHjNmjPTs2VO0x6emIkWKSPPmzU3v67Zt28qpp55q9h88eFAGDhwoN998s3kf6H9efn8IVD77EEgmgaw+117+bPWyrGS6d7QVgZwU4Hdlb3QZAu+NI6UgkFAC69atM0PIly5dGnG7mjVrJi+//HLE+efOnStTpkwx+QsVKiSPPvpowHNXr15thujpQQ2O3HPPPQHzuXdWr17d/ZZtBJJeICvPtBvNy+fw3nvvlR9++MEUX758efn0009Fv3/Yaf78+SZA+vvvv8sXX3whd955p4wbN84+zCsCCFgC2Xmm16xZI3fffbfjqH98GD58uFgjPpx9+oeHJ598Ul544QWxemfIxIkT5fLLLw84vYyX3x+cCrCBQBIKZOe59vJnq5dlJeFtpMkIBBTgd+WALLm/M7e7nHI9BBCIXQGrF4fvueee8xUuXDjTkLhwQ+CjadW2bdvMkHrrO565zsiRI4Oebs0P6NTl1ltvDZqPAwggkFnAq2faq+fQmufTV7BgQfNMW3/48FmBk8yVtvZYvb59pUqVcvJZPdED5mMnAskm4MUzfdVVVzk/V62RHiEJrd6fTt5g/w7w6vtDyIpwEIEEFsjuc+3lz1Yvy0rgW0bTEMhRAX5XzjlehsDnfsyZKyIQkwLLli2Tli1bysMPPyxpaWmmjtWqVfO8rtY/8uTaa68Va24hU/btt98u+hUsLVmyxDlkzTHmbLOBAAKhBbx8pr16DrWXuDWXoKl43759zUIrgVrRoEED0R4omo4dOyajR48OlI19CCSVgBfPdHp6uuiiJnZ69dVX7c2Ar08//bRYfxQ1x/Tn9saNGzPl8+r7Q6aC2YFAEgh48Vx7+bPVy7KS4PbRRAQ8F+B3Zc9J/QokAOrHwRsEklNg5cqVZgjqTz/95AAMGDBAPv/8c+e9Vxs6pO777783xelQ9Zdeeilk0YsXL3aOu4fJOjvZQACBTAJeP9NePYc6pN1OOuw2VOrVq5dz+I033pCTJ08679lAINkEvHqmdRVanWNQU+nSpYP+EcL21eBnw4YN7bfi/l5g73Tv4+e0rcIrAuEFvHquvfzZ6mVZ4QXIgQACGQX4XTmjiLfvCYB660lpCMSlwIEDB5xeWZUrV5bp06fLa6+95vT68KpRa9euleeff94pTq9RtGhR532gDbtniTVsVho1ahQoC/sQQCCDgNfPtBfP4f79+0XnCtSk8/62adMmQ63939arV0+sIbdm59atW82ch/45eIdA8gh49UzrPJ/vvPOOmdvziSeeiAjQvRCZ/izOmLz4/pCxTN4jkAwCXjzXXv5s9bKsZLh/tBEBrwX4Xdlr0czlsQhSZhP2IJCUAjrc/ZFHHhEdlnrKKafkiIEuYKTDWTVdffXVZkGFUBfasmWL01NFe6BovXT4rAZR9Ovw4cPSuHFj0zvFHqIXqjyOIZBMAl490149hwsXLjSLqeg9OOOMMyL6PlO7dm1nuoxVq1aJBkVJCCSrgBfPdMWKFc1K75EaakBEF02yk64U705efX9wl8k2AskkkN3n2sufrV6WlUz3kLYi4JUAvyt7JRm8HAKgwW04gkDSCGgQccOGDRKoZ4dXCFOnTpUvv/zSFKe9Pl955ZWwRdu9SjSj9v4cMWKE6Hxk27dv9zu3QIEC0r17d9NrtWzZsn7HeINAMgp4+Ux79RxaE7o7t0IDoJEk/cVw3rx5Juv69esjOYU8CCSkgJfPdDRAQ4YMEZ03VFOVKlWkUqVKfqd79f3Br1DeIJAkAl48117+bPWyrCS5hTQTAc8E+F3ZM8qQBTEEPiQPBxFIDoFixYrlaPDTWsdNBg8e7GDqokf6i1S45J5XbMKECdK/f/9MwU8tQyeLnjRpkukJOmPGjHDFchyBhBfw8pn26jnUoX52Ou200+zNkK9lypRxjrvPd3aygUCSCHj5TEdKpj0/3fN033nnnZlO9er7Q6aC2YFAEgh48Vy7fzZm92erl2Ulwe2jiQh4JsDvyp5Rhi2IHqBhiciAAALZFfjoo4+cuf90GPuDDz4YUZHuniXaA0X/odiuXTvzVb9+fTMn4HfffSdTpkwx5e3cudP0BF26dKnUqVMnomuQCQEEQgt49RzqUFo7FSlSxN4M+eqe2kKnvCAhgEDuCPz666/SqVMnOXr0qLngWWedJffff3+mi3v1/SFTwexAAIGIBLz82eplWRFVnkwIIGAE+F059z4IBEBzz5orIZC0AkOHDnXarnOMnn766c77UBvuX6z0HB1C36BBA+eULl26yH333WdWq+/du7eZKzAtLU1uvfVWmT17tqSkpDh52UAAgawJePUcunuWZCUA6l6IJWst4SwEEIhEQFeJv/TSS2Xz5s0mu/7xUf/QGOi59er7QyT1Ig8CCGQW8PJnq5dlZa4pexBAIJgAvysHk/F+P0PgvTelRAQQcAl8//33zhx+Olfnww8/7DoaelN/GOj8Yzr0XXt6uoOf7jO1l8rw4cOdXXPnzpVp06Y579lAAIGsC3j1HLrnGNZpKyJJJ0+edLLl1OJszgXYQAAB0UWNzj//fFmxYoXR0Dm7tWdKsJ+/Xn1/gB4BBLIm4OXPVi/LylprOAuB5BPgd+Xcvef0AM1db66GQI4LPP/88+L+C26gC2rPDv0FJzfSmDFjnMvodatXr+68D7fRo0ePcFmc49dee61ZWGn+/Plmn65kqSvNkxCId4G8fqa9eg6LFy/u3ArtqR1JcucrVapUJKeQB4GYF8jrZzoYkM7nedlll8mOHTtMFg1+fvzxx9K+fftgp4hX3x+CXoADCMSBQF4+017+bPWyrDi4bVQRgWwLePHs87tytm9DVAUQAI2Ki8wIxL6ALlhg//ISrLYlS5bMlQCo9vKaPn26U43rr7/e2c6JjVatWokdALV7r+TEdSgTgdwUiKVnOpJ2B3sO3b9YRTqc3T3vp37fIiGQCAKx+Ex//vnnJph56NAhQ1yuXDkT/GzZsqWn5MG+P3h6EQpDIJcF8vKZ9vJnq5dl5fIt4HII5IlAdp99flfO/dvGEPjcN+eKCCSNwKxZs+T333837dU5xK688socbbsu0mCndevW2Zu8IoBALgoEew4rV67s1GL79u3OdqgNXYjFTqeeeqq9ySsCCHgoMHr0aOnatavYwc/atWubPyZ6HfzUKgf7/uBhcygKgaQS8PJnq5dlJdVNoLEIZFGA35WzCJeN0+gBmg08TkUgFgXuuOOOsEPgmzVrlitV/+CDD5zrXHHFFWYVd2dHDmy4e5VFutBSDlSDIhHwVCCWnulIGhbsOaxfv75zus4zGEmyF2HRvI0aNYrkFPIgEPMCsfRMDx48WJ5++mnHrHXr1vLhhx9K2bJlnX1ebgT7/uDlNSgLgdwWyMtn2sufrV6Wldv3gOshkBcC2X32+V059+8aAdDcN+eKCOSowBNPPJGj5UdT+Ndff+1k13nFokkzZ84U/aGwa9cuqVq1qugQg3BpzZo1Tpa6des622wgEM8CeflMe/kcaq8yXcjo6NGjsm3bNtm5c6dUqFAh6K05ePCgrF271hzXhRmaNm0aNC8HEIgngbx8pt1ODz74oLzwwgvOLp1Le+zYsVK4cGFnX6gNL78/hLoOxxCIdYG8fKa9/NnqZVmxfs+oHwJeCGT32ed3ZS/uQnRlMAQ+Oi9yI4BAhAK7d++Wn3/+2cndsGFDZzuSDV38ZMSIETJlyhR544035MiRIyFP09WidfV3OwVbsdY+zisCCIQX8PI5LFCggFx88cXORd3/6HN2ujZ0WJC9WrwGPyMNyriKYBMBBIIIPPPMM37BzwceeEDefffdqJ4zL78/BKkmuxFAIIyAlz9bvSwrTLU5jEDSC/C7ct58BAiA5o07V0Ug4QUWLFjgtDF//vwSbY/Mtm3bSqFChUwZ+kvWuHHjnPICbYwcOVJWrVplDunQvd69ewfKxj4EEIhCwOvnsGfPns7Vhw0bJj6fz3mfcePFF190dv3tb39zttlAAIHsCejPyieffNIp5JFHHjHB0JSUFGdfJBtef3+I5JrkQQCBzAJe/mz1sqzMNWUPAgjYAvyubEvk7isB0Nz15moIJI2A+5u6PaQmmsbrokk6HM9O2jtl6dKl9lu/1y+++EIGDRrk7Hv88ceFFaMdDjYQyLKA189hjx49pHr16qY+ixYtkr59+4r23nYnDYreddddMnv2bLO7UqVK0qdPH3cWthFAIBsC/fv3l+PHj5sSOnXq5DcHaDTFev39IZprkxcBBP4S8PJnq5dl/VVDthBAIKMAvytnFMml99YvGiQEEEAgoMDGjRu1e5b5KleuXMA8wXZef/31zrnXXHNNsGwh9x84cMBXp04dpxyrR6jP6qnimzNnjk/r9tFHH/n69evns3qtOHk6dOjgO3bsWMhyOYhAsgpk5Zn2+jmcPn2687zq95eOHTv6Jk6c6Fu3bp1v0qRJPmvBNOe4PtuTJ09O1ttFuxEIKxDtMz1hwgTn+dLnr3jx4r4yZcpE/PXaa6/51cnr7w9+hfMGgSQViPa5ViYvf7Z6WVaS3kKajUBYAX5XDkuUIxl0+BkJAQQQCCiQlX+A2QVddNFFzi9Z1gTR9u6oX1esWOGrVauWU5YdkA30OnDgQJ81Z2DU1+AEBJJFIKvPtNfP4fvvv++zeo+Ffa6tYfDJcmtoJwJZEoj2me7atWvY5y7Qz1d7n7VoUqZ6ev39IdMF2IFAkglE+1zbPF7+bPWyLLt+vCKAwF8C/K78l0VubjEE3voXHQkBBLwX+O2335xCo10AyTnR2tDFjFauXCnPPfeclCpVyn3IbOs8oc2aNROrB5nonIE63ygJAQS8FfD6OdQhdjr0p2XLlqKLLmRMzZs3l2+//VasP2pkPMR7BBDIhoD+PPU6ef39wev6UR4CySLg5c9WL8tKFn/aiUA0AvyuHI2Wd3lTNNrqXXGUhAACCOScQHp6umzZskXWrFkjO3fuFA2sNmrUyFksKeeuTMkIIGALeP0c6iJnOr/v6tWrRef71DmDa9SoIdEuyGLXj1cEEMg7Aa+/P+RdS7gyAvEt4OXPVi/Lim9Vao9AbAvwMzj8/SEAGt6IHAgggAACCCCAAAIIIIAAAggggAACCCAQpwIMgY/TG0e1EUAAAQQQQAABBBBAAAEEEEAAAQQQQCC8AAHQ8EbkQAABBBBAAAEEEEAAAQQQQAABBBBAAIE4FSAAGqc3jmojgAACCCCAAAIIIIAAAggggAACCCCAQHgBAqDhjciBAAIIIIAAAggggAACCCCAAAIIIIAAAnEqQAA0Tm8c1UYAAQQQQAABBBBAAAEEEEAAAQQQQACB8AIEQMMbkQMBBBBAAAEEEEAAAQQQQAABBBBAAAEE4lSAAGic3jiqjQACCCCAAAIIIIAAAggggAACCCCAAALhBQqEz0IOBBBAAAEEEEAguQR8Pp/s2bNHdu7cKUeOHJGyZctK+fLlpVixYskFQWtzVeDEiROybds289mrWrWqVKhQIVevz8UQQAABBBBAAIFEFaAHaKLeWdqFAAIIIIAAAlELfPXVV9KnTx8TeNKAZ6NGjaRFixZSs2ZNKV68uDRu3FgeffRR2bBhQ9Rlc0LOCBw8eDBowffee680adLEfH3++edB8+XlgdWrV8sDDzwgZ5xxhpxyyilSo0YNad68uVSsWFFKlCghTZs2lWHDhplAfG7Wc8CAAY6dPhckBBBAAAEEEEAgngUIgMbz3aPuCCCAAAIIIOCJwI4dO+Tyyy+Xjh07yvjx42X37t0By12+fLkMGTJEGjRoYAKhx48fD5iPnTkvcPLkSRk+fLhccMEFQS+2adMmWbp0qfnau3dv0Hx5cWDr1q1y8cUXS/369U2Ac/PmzZKenu5XlUOHDsmSJUtMgFQDo6+//rrf8Zx847bbv39/Tl6KshFAAAEEEEAAgRwXYAh8jhNzAQQQQAABBBCIZQENjGngc8WKFU41CxYsaHrkVa5cWUqXLi3bt28XDVDt2rXL5Dl27JgJhG7ZskUmTJggKSkpzrls5LzA77//boKHGhzUnpPxlrQ36o033ijaDjsVLlxYzjrrLNMDVKdc0M/WmjVrzKvm0ekY7rrrLjM8/oknnrBP4xUBBBBAAAEEEEAgAgECoBEgkQUBBBBAAAEEEldg4MCBTvCzQIECprfdfffdZ4YgZ2z1zJkz5R//+IfMmzfPHHrnnXekWbNmcv/992fMyvscFND5WTX4GS61bNlS8uX7c8BTlSpVwmXPleMa/OzSpYvoPLOaNMB+9913iw7XP+200zLV4aOPPpLBgweL9j7W9OSTT0rJkiX5zGWSYgcCCCCAAAIIIBBcIMX6x9ef//oKnocjCCCAAAIIIIBAQgpoz04dWmwPZR81apTcdtttIduqedu2bSvz5883+SpVqiQ6XFjnbyTljsDatWulbt265mLaA1T94yFp4Fbnlf3tt99MdXWho2+//VbOPPPMkNU/cOCAXHjhhWYov2bMnz+/mYc2J3u/apD2s88+M/WaPHmyXHPNNSHryEEEEEAAAQQQQCCWBZgDNJbvDnVDAAEEEEAAgRwV0J6cdvCzWrVqYYOfWhkdHj9x4kSnXjp/qAaxSAiEE7jzzjud4GeZMmVk9uzZYYOfWqb2+NRgpC6KpEnnP3355ZfNNv9DAAEEEEAAAQQQCC/AEPjwRuRAAAEEEEAAgQQV+OWXX5yW2T0KnR0hNrTXqC6EtHLlSpNLXzt16hTijL8OafBKezCuWrVKdI5R7RGoK8xnNem8pMuWLTPzk1aoUMGUpyvYB0oa7P3vf/9r8mp7a9WqZQK6gfIG26fzn27YsMHMT6nl6RDuevXqiQaQs5p+/fVXUy+d+1JXbc+OR1brYJ+nc2/+/PPPJlCpvXvVSO9Tdud51TZOnTrVvow89NBDpvexsyPMhtbl1ltvlZdeesnkfOutt8xwePUPlXQhJV1pXnvJVq9eXRo2bCjFihULdUrYY1qWTkGgPVj1funUEdEmL5+DEydOyJw5c+To0aPStGnTgNNXuOvn5bXtcr36DHvx+cuJ9tnt5BUBBBBAAIG4FdAh8CQEEEAAAQQQQCAZBcaOHatTAZkvK3joS0tLi5jB6vXps4YG+6x5QX3Wit5hz7OGzPtatWrlsxa7ca6p17bmqPTVqVPHN2bMmJBldO3a1Wf1PjVfVoDDZ63M7evVq5fPCj75lad5br75Zp8VnHTKW7x4sc9a6MlnBb788lqBUp81j6mTL9iGFSj2Wb0XfVYw0GcNv/Yrw/azFvDxWfNbBivC566/Fagy1v379/dZQTm/8tTDCtL5rIBWprL+85//OAb2dfXVdilXrpzfOVdddZVz7L333vM75n5jrb5u/Fu0aOFXF/sa2rZJkyb5NF9W0wsvvOCUbQUtfVZgMuqi9D7cdNNNvpEjR/qsAHrI+lhzh/qsIL1zTbst6qv38Y033gh5/c6dOzvn6udc75k1N67PClI7+7XMokWL+qwpIXxWr+iQ5dkHs/scXHzxxeaeWlNOmCL183vqqac6ddLn4ZZbbrEv5/ea3Wt78Rn2q9D/3nj1+ctu+wLVjX0IIIAAAggkioBOwE5CAAEEEEAAAQSSUsDqiemzevY5wZPevXv7rPkWPbXQQOTjjz8eNHBoB6b01Zpn0WetDB7w+u6AlDWHpO+cc85x6u0uw962FmYy5SxYsMAvQGQfd7++8sorAa+pOzWQVqRIkZDXcpelQc1AyV3/3bt3+y666KKQZWqg9cUXX/QravTo0SHP0cCiO2nAyq5bsECvBpK7devm5LPzB3rt0aNHyKCj+9oZt88991znGu3atct42LP3anvppZc61wrUDnufBsWDBe/d90vvg7WgVNgyH3vssaDt8Oo5UDutvz63GvALFJDX++lOXl3bbZLVz7C7XrrtxefPq/ZlrBvvEUAAAQQQSCQBAqCJdDdpCwIIIIAAAghELXDllVf6BXaslbh9Gsj5/vvvfdbQ2qjLy3iCNWzZr/yePXv6tDeiNWzd9+mnn/qsVeV91pBvJ4+1qrxPe3hmTO7gizV83eTXXqva21ODe9o7UgNadnBLXx999FGfNW+k2WcN0fcNHz7cZw3D9j3wwAO+ihUrOnm1B521QE/GS/qsYb2+QoUKOfk06Prmm2/65s6d61uxYoXviy++8A0dOtSn9XBfV3ucZkzu+jdu3Njkt+a2NHV5++23ffqlPffcvfk0uGUNCXaKWrNm7horswAAQABJREFUjbn+v/71L+d6er+0Tvo1btw4J69uRBIA1R6F7rrfcMMNpjejteq66d17xx13mF66dp5HHnnE7xqRvnE7BgsSR1pWqHxXX321X3s0qK+2ixYtMj2WrRXn/XoNaw/cw4cPZyrSfb/sPxLo/dLytffo9OnTff/85z/97pfms6ZYyFSW7vDqOXAHQLWnsH1fatasaXqj6vtp06b51cGra7tNsvoZ9quY9caLz59X7ctYN94jgAACCCCQSAIEQBPpbtIWBBBAAAEEEIhaQHtc6hB0O5DiftUehRrwee2110zAL9oh0AsXLnR6mOrQ3ClTpgSsnw5nrl27tlMHa37HTPncwRetowauMgYttX6XXXaZU47dFh0ynTEtXbrUL/AaaAiz9ly1y+jQoYPfsHp3eQcPHvQzDDQEOWP9dToAawEpdzFmW3usunucBupVqIFQu17WSuiZyrB3hAuAuqdA0KkJgg2THz9+vHM9DcoGqrd9zUCv2lvQrq++6ucpJ5IGOu3raLAyYyDQvuZ3333ns+YUdfIGCupmvF8a5LbmRrWLcF63b9/usxZ0csrS4HrG5OVzYAdA7XZqEFT/mKDpyJEjvhkzZviseWqdKnh57Ywm2fkMawW9+Px52T4HjQ0EEEAAAQQSUIAAaALeVJqEAAIIIIAAAtEJaBD0+uuvd4I4dnAl46vOmdm3b1/fV199FbCXpvuqGoxs3bq1U+bgwYPdhzNt//jjj06wVINNOjTWndzBFw0Qao++QOnLL790rqn114BosKTzetpt1F6V7qTzoWp77ePWQk/uw5m2R4wY4eQ977zzMh1311+DwaHKe+qpp0KW5VUA1Fq8ybmOPWVApor/b0eXLl2cvNZCRMGyBdyvwWbbUV913livkwZZNWBvXyfj9AEZr6c9OO28OoeqtVCSXxb3/dJ5PvXzGSw9++yzTlkaFHQnr58DdwBU6x2ot7F9fa+v7TbJ7mdY65jdz5/X7bPdeEUAAQQQQCARBfJZ//AhIYAAAggggAACSS1g9WATqwekWMEUsYKCYgUgA3roiuvWUHO55JJLpHnz5vLDDz8EzKc7tax58+aZ41bAUqwAaNC8esCaI1KseTFNnp07d4q1oFDQ/NYcj2a160AZrAV7/HYPGDDA7737jdXr1Hm7yVrZ252sf/iKtfCPWPNuyrBhw6R+/fruw5m23WVZQ6ozHXfvuOKKK0KWZwWOnexWL1dn28uN9evXm9XRtUwrwCcPP/xwyOKtoeNiBcnFGvYt1lQAIfNmPLh3716/XdWqVfN778Ubqyeg7Nu3zxRVpUoVsYbZhyzWmvpBbGdrDkmxFjoKmt8KopvPZ7AM1uJRziHrjwnOtm7k5HNwwQUXmFXo/S7oepOT187uZ9iLz19Ots/FyCYCCCCAAAIJIVAgIVpBIxBAAAEEEEAAAQ8EmjRpIv/+97/l9ddfF2tYrVhzXIrVW0+sIcOSMahn9cA0ASQNiFrzcGa6+rp165x9Gty0Vq123gfb0ECSXk+TBkiCJQ2+BkvWSuh+h84880y/9+431tyjzltrlW9nWzesIeFi9bYzX34HArzRgK0G4OykAbVQyVqFPNRh0YC0nazhzPamp6+2sxZ69tlnBw162xe15lAV/cpKslZO9zvN6t3r996LN1YPTqcYDVjq/QuXrOkdnCC91as2aHZrAaSgx/SAO6BrB2HtE3LyOXAH3e3ruV9z8trZ/Qx78fnLyfa5HdlGAAEEEEAgEQQIgCbCXaQNCCCAAAIIIOCpgLWYiwmKaWDsoYceEg3oWYu7yCeffCLvvvuubN682VxPe0laC5CYAFD79u396uAOYGrvSmv4rN/xQG+sFbmd3e7znZ3/28gY5HQf17q7kzs45d6v29Z8lhl3hXxvDbMWDZRp3dauXSsagLHmLzWvIU/McNCavzTDHv+37uCdtSCU/0GP3lkLPDklhTJyMmVjw5pv0+/snOjV6g6AWvOi+l0v2JsaNWo4h/R+BkvWQlPBDpn91gJPznFr4TBnWzfcn2Ovn4NwQcicvHZ2P8NefP5ysn1+N5E3CCCAAAIIJIAAAdAEuIk0AQEEEEAAAQRyVsCaa1DatGljvqy5MsVaBVsGDhxoAqMaoLMWC5JQAVBroRjRr2iSO7iR8TxrZfeMu4K+17pnJ2nPUGtxHXn55ZfFmrczO0U554YK4DqZcnhDe63aqWrVqvZmjrxqj1btAWz3srVWtvf8OtkNgLp7E2asXLFixTLuivi9+3Ps9XNgrfwesh45ee3sfoa9+PzlZPtCwnIQAQQQQACBOBQgABqHN40qI4AAAggggIA3AgcOHBCd19NaTESsleAjKtRa/ETuuususVacNr1D9aTvv/9etEfX6aef7pThHjKvPQyDzSvqnJBhI1RQTuuQG0l7fOo8i4F6B2pg1VrExczB2LFjR9Hh9N26dYuoWhl7qUZ0kseZ3L1fc6M+Ot/mt99+a1qhw5+153BWkrXgkJQqVcpMTeCel9XdUzbS9rh7a4b6TEVaXqD25ORz4O55mtvXzo6J1tWLz19O2gbyZB8CCCCAAALxLJA7/3qOZyHqjgACCCCAAAIJKaC9Gnv37m3apoFLHX6eL1++iNt63XXXmYVzdBi8pg0bNvgFQN3zE3bv3t0sJBRx4TGQUQO8OpekHfzUgM0NN9xgFoBq3LixCX66e5d+/fXXTq1tE2dHDG6450Z1Tz2QU1XVRXPsAKhaqVG0QbQ//vhDnnjiCdPzWOv59NNPy6BBg0yV9fNmL8r1yy+/mH3h/ufOV758+XDZs3Q8L5+DvLx2OCwvPn+x3L5w7ec4AggggAACuS0Q+b/yc7tmXA8BBBBAAAEEEMhBgbp16zqla+9N7cUZTdJ5HUuWLOmckjF46u5Rqqs1x1v68ssvnYWNdE7Ozz77TMaNGyc33nijaADUHfzUtmlwzk7u3oj2vlh7dc8fuW3btrDV04DwY489JqNHj3YWqgp7kiuDBkDtpD1rNQAfbXr//fed4Kd+3vRe2MkdDHMHNu3jgV7tuWz1WE4FQPPyOcjLawfydu/z4vMXy+1zt5VtBBBAAAEEYkGAHqCxcBeoAwIIIIAAAgjkuoAOH9a5NA8ePGiu/d5775nh3pFWZNasWeJezdsdjNAy3O9/+ukn2bt3r5x66qkhix86dKgsWLBAdBEbnVNUe2DmVZo9e7Zz6Ysvvtj0/HR2BNhwrwIfbwHQJUuWhL0/c+bMkWeeeca0vHnz5mZRrAAMQXfpfJWXX365WUhLMz366KOiPYMjnV9Tp2qwr6/nd+nSxW/1dXcAVIPVx44dk3BDxKdOnapFmaRTHeREysvnIC+vHc7SHQDN6ucvltsXrv0cRwABBBBAILcF6AGa2+JcDwEEEEAAAQRiQkADT3fccYdTF13YSFd4jyRp4HPIkCFO1rZt22bqQadBMnuYqwZZtfdgqKQBrn/+85+iQalhw4aJzk+al0lX7LZTuFXFdWX4sWPH2tnFPbeks9PDDV1QyE4a6MtK0h7AjRo1MqceOnTILPIUqhz3Z+Oqq64KlTXoMV1Iyq679jrWgOi+ffuC5rcPHD9+3ARL3UP17777bvuweb300kulbNmyZlt7durnOVT69NNPZf78+U6WHj16ONtebuTlc5CX1w5n6MXnL5bbF679HEcAAQQQQCC3BQiA5rY410MAAQQQQACBmBHQldztoJEuhHT99debL+2xGSywpj0jzz77bHHPeRkouKmBLg142WnkyJFm+LT93v2qw6u1N2BqaqrZrXOSXnPNNe4sub6tw9ztNGPGDNEgYaC0ceNG0R6ie/bscQ7bvWqdHR5vuHtNauD4t99+i/oKOoTcfX90231P3QVOmzbNDP/XfdqrMqvBQg2IP/TQQ07R2ou4VatWpldosHlTNU/Lli3lu+++c87Tz5suPOVO2rtY5wS1k84Vqj1BAyX9fPfv39851KFDB7OYlbPDw428fA7y8trhCL34/MVy+8K1n+MIIIAAAgjkuoD1jy0SAggggAACCCCQtALWwjG+okWL6kpGfl/WHJe+Jk2a+Pr27euzVuz2WYEqnzXnp18ePef//u//QtpZw9j9zrF6/fkmTJjgW7Fihc+aG9Q3atQonzUc1smj17UCV5nK7Ny5s5Nn8uTJmY7bO6xgqpNP62f1xrQPZXp96623nLzWAkd+x3/88UeftfCRc1zbb61e7rN6LPqsYKfP6qnqu/fee31WsNbksYIxPiuoY7atxX18Vq9Fv/Iirb+eZPUoda5buXJlv3L0jTXE3metOu/kqVKlis9alMpnBZHNMfuErl27Onneeecde7ffq7VyvZNH63/PPff4PvroI9+6det8X331lc/qJey0Sz2tnpV+50f7xgq0++6//37nmvbnTj8Dt912m89a5d334osvmu3zzjsvUz69T1pGoKQu5557rnOO3od+/fr5pkyZ4rMWs/JZvT59jzzyiE/vlX1d6w8Avu3bt2cqLpr7ZfU4dcorU6ZMprJ0h1fPQbt27ZxrffzxxwGvlXGnV9eOxiTcZ9iuoxefP6/aZ9eJVwQQQAABBBJRQFegJCGAAAIIIIAAAkktYA0F9jVs2NAJrNjBoVCvGjR9/vnnw7pZvSFNECpUWfaxAgUK+Kyh1gHLjDT44lUAVCvxr3/9KyITa/5J36JFi3wtWrRw8luLKPm1I9L660mRBI/uvPNO51q2n75u2LDBuW4kAVBrqgHfzTffHLAsd7m6rQFfr5J+dqzepBFdV69dpEgRn9V71Hf06NGQVbB6xJpAcMa6B3rfoEED36pVqwKWF839iiQA6tVzkJUAqFfXjsYkks+wwnvx+fOqfQE/COxEAAEEEEAgQQQYAm/9a5CEAAIIIIAAAsktYPW0E12pXedN1OHcoRaP0dXfdfjwypUr5e9//3tYOKunoowYMUKs3oRmmLEV5Mx0ju675ZZbxAqaSK9evTIdz6sdgwcPlg8++MCZy9RdDx3Cq4uwvPTSS7J06VJp2rSp37DsiRMnurN7vj18+HCxekxKuXLl/MpWw2iSLoQ1ZswYM/fqWWedJVavyUyna9t0EST3kPlMmaLcoZ8dndPzqaeeEquXa9Czdbi/1QNZ1q9fL88991zIz6YWoh56z6wer6ILIwVqjy7IZPU0NQs51atXL+i1vTyQl89BXl47nKEXn79Ybl+49nMcAQQQQACB3BJI0UBubl2M6yCAAAIIIIAAAvEgoPNdWj3jzLyWu3fvFl3VvGLFimINtRarp6ho8C+rSecWtYYjmwCqzuGnwSidG1KDGLGatP26KJLVu9KYaODT6j0Y8QrmOd2uLVu2mFXcq1WrJjoXZnbS4cOHZfny5aatVatWFQ0Qli9fPjtFhj1X/zmu85hu27bNBEV1YSRdeEqdNTgaKIgZttD/ZdD2aLBeP3PW8HRTbv369bNVZqTXDpUvL5+DvLx2KBM95sXnL5bbF679HEcAAQQQQCCnBAiA5pQs5SKAAAIIIIAAAggggAACCCCAAAIIIIBAngtkvftCnledCiCAAAIIIIAAAggggAACCCCAAAIIIIAAAqEFCICG9uEoAggggAACCCCAAAIIIIAAAggggAACCMSxAAHQOL55VB0BBBBAAAEEEEAAAQQQQAABBBBAAAEEQgsQAA3tw1EEEEAAAQQQQAABBBBAAAEEEEAAAQQQiGMBAqBxfPOoOgIIIIAAAggggAACCCCAAAIIIIAAAgiEFiAAGtqHowgggAACCCCAAAIIIIAAAggggAACCCAQxwIEQOP45lF1BBBAAAEEEEAAAQQQQAABBBBAAAEEEAgtQAA0tA9HEUAAAQQQQAABBBBAAAEEEEAAAQQQQCCOBQiAxvHNo+oIIIAAAggggAACCCCAAAIIIIAAAgggEFqAAGhoH44igAACCCCAAAIIIIAAAggggAACCCCAQBwLEACN45tH1RFAAAEEEEAAAQQQQAABBBBAAAEEEEAgtAAB0NA+HEUAAQQQQAABBBBAAAEEEEAAAQQQQACBOBYgABrHN4+qI4AAAggggAACCCCAAAIIIIAAAggggEBoAQKgoX04igACCCCAAAIIIIAAAggggAACCCCAAAJxLEAANI5vHlVHAAEEEEAAAQQQQAABBBBAAAEEEEAAgdACBEBD+3AUAQQQQAABBBBAAAEEEEAAAQQQQAABBOJYgABoHN88qo4AAggggAACCCCAAAIIIIAAAggggAACoQUIgIb24SgCCCCAAAIIIIAAAggggAACCCCAAAIIxLEAAdA4vnlUHQEEEEAAAQQQQAABBBBAAAEEEEAAAQRCCxAADe3DUQQQQAABBBBAAAEEEEAAAQQQQAABBBCIYwECoHF886g6AggggAACCCCAAAIIIIAAAggggAACCIQWIAAa2oejCCCAAAIIIIAAAggggAACCCCAAAIIIBDHAgRA4/jmUXUEEEAAAQQQQAABBBBAAAEEEEAAAQQQCC1AADS0D0cRQAABBBBAAAEEEEAAAQQQQAABBBBAII4FCIDG8c2j6ggggAACCCCAAAIIIIAAAggggAACCCAQWoAAaGgfjiKAAAIIIIAAAggggAACCCCAAAIIIIBAHAsQAI3jm0fVEUAAAQQQQAABBBBAAAEEEEAAAQQQQCC0AAHQ0D4cRQABBBBAAAEEEEAAAQQQQAABBBBAAIE4FiAAGsc3j6ojgAACCCCAAAIIIIAAAggggAACCCCAQGgBAqChfTiKAAIIIIAAAggggAACCCCAAAIIIIAAAnEsQAA0jm8eVUcAAQQQQAABBBBAAAEEEEAAAQQQQACB0AIEQEP7cBQBBBBAAAEEEEAAAQQQQAABBBBAAAEE4liAAGgc3zyqjgACCCCAAAIIIIAAAggggAACCCCAAAKhBQiAhvbhKAIIIIAAAggggAACCCCAAAIIIIAAAgjEsQAB0Di+eVQdAQQQQAABBBBAAAEEEEAAAQQQQAABBEILEAAN7cNRBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhjAQKgcXzzqDoCCCCAAAIIIIAAAggggAACCCCAAAIIhBYgABrah6MIIIAAAggggAACCCCAAAIIIIAAAgggEMcCBEDj+OZRdQQQQAABBBBAAAEEEEAAAQQQQAABBBAILUAANLQPRxFAAAEEEEAAAQQQQAABBBBAAAEEEEAgjgUIgMbxzaPqCCCAAAIIIIAAAggggAACCCCAAAIIIBBagABoaB+OIoAAAggggAACCCCAAAIIIIAAAggggEAcCxAAjeObR9URQAABBBBAAAEEEEAAAQQQQAABBBBAILQAAdDQPhxFAAEEEEAAAQQQQAABBBBAAAEEEEAAgTgWIAAaxzePqiOAAAIIIIAAAggggAACCCCAAAIIIIBAaAECoKF9OIoAAggggAACCCCAAAIIIIAAAggggAACcSxAADSObx5VRwABBBBAAAEEEEAAAQQQQAABBBBAAIHQAgRAQ/twFAEEEEAAAQQQQAABBBBAAAEEEEAAAQTiWIAAaBzfPKqOAAIIIIAAAggggAACCCCAAAIIIIAAAqEFCICG9uEoAggggAACCCCAAAIIIIAAAggggAACCMSxAAHQOL55VB0BBBBAAAEEEEAAAQQQQAABBBBAAAEEQgsQAA3tw1EEEEAAAQQQQAABBBBAAAEEEEAAAQQQiGMBAqBxfPOoOgIIIIAAAggggAACCCCAAAIIIIAAAgiEFiAAGtqHowgggAACCCCAAAIIIIAAAggggAACCCAQxwIEQOP45lF1BBBAAAEEEEAAAQQQQAABBBBAAAEEEAgtQAA0tA9HEUAAAQQQQAABBBBAAAEEEEAAAQQQQCCOBQiAxvHNo+oIIIAAAggggAACCCCAAAIIIIAAAgggEFqAAGhoH44igAACCCCAQA4KHD58WHbt2iU+ny8Hr0LRCCAQjwKbVh+X9HS+N8TjvaPOiSWgP6N9m1clVqNoDQIIJJ1AgaRrMQ1GAAEE4ljg9ttvl7S0NNOClJQUee6556RixYpRt+jEiRPSt29f57x27dr5vXcOJPjGnDlzZPTo0U4rX3zxRSlXrpzzno2cEVizZo08++yz8uGHH8r+/fvNRQoWLCinn366DBkyRHr16hX2wqmpqdKvX7+w+bKS4fzzzxd91uI1LViwQF5//XWn+mpduXJl5z0bCMSDwPFjPhn11CHp1KuItO1aOB6qTB0RSFgB31dvS/rLd0i+h8dJvrY9E7adNAwBBBJbgABoYt9fWocAAgkm8O6778qhQ4ecVmnwaPr06c77SDfS09Pl7bffdrIXLlw4KQOgP//8s5/DP//5TwKgzqciZzZ+++03ufjii2X79u1+Fzh+/Lhs3rxZSpcu7bc/2BvN7/4MB8uXlf36x4V4DoD+8ssvfjYPP/wwAdCsfBA4J08Fvv4gTfbsSJfp/0mV5hcVkqLFGbiWpzeEiye3wP7df7Z//57kdqD1CCAQ1wIEQOP69lF5BBBIdgHtQTdhwgS58cYbk52C9seBgAYtu3fvnin46a56w4YN3W/ZRgCBCAX0j2GrVkU+RLVmzZoR9baO8PKeZtv/R7p8OiHVlHlov08+HndErh1QzNNr5EZhU6dONT3Vy5YtG9W90brNmjVLxo8fL+vXrzd/HCpTpozUrVtXrrvuOunatavky0dAODfuoV4jK/cxkZ7H3HL28jpHjx6VsWPHyqJFi8wztGnTJvMHbn2G9OuWW26RSpUqRXxJnseIqciIQEwLEACN6dtD5RBAAIHwAvfcc4906NAhqn/IhS+VHAh4L6C/QMybN88p+IwzzjBD3ps0aWLmAN25c6dUqVLFOR5qo0CBAnLRRReFymKOrV69WrTXqZ1atGghxYqFDqTUq1fPzs4rAnEj8NRTT8lPP/0UcX07duwYswHQKaNS5eifs72Y9nw7Pc0Mg69YLX/E7cvrjLt375b+/fuLvkaT9uzZI71795bPPvvM77StW7fK0qVLZdKkSdKjRw/zx89ChQr55eGN9wJZvY+J9Dx6r5qzJU6ePFl05IMGPd1JR0csXLjQ7NLpdnTUz9133y3674lgiecxmAz7EYhPgeBPe3y2h1ojgAACSSewd+9eueOOO+Sjjz5KurbT4PgSWLlypV+FR4wYIZ06dXL2aa+MSJMGMWfOnBk2+8033yzjxo1z8ul2NNdxToyjjXPPPVfU1k7R9HKxz+FVrMV3RLbsFPnjgIjVeVlOLSlSpbxI0RicjlLndV6xYkVC3LZNa07I/C+P+rXl5EmRSf8+LPcOsW5CHCT9uXzJJZeI/lEnmnTkyBHTu/OHH35wTtPvV40aNTLB7Y0bN5r9GuA5cOCA6ZlYtGhRJ29ebOz/aLmkH3RFq7NRiVLdm0i+wgWzUYK3p2b1PibS8+itaM6XNmbMGL8pnYoUKSI6sqRWrVry66+/yrJly0Tv68GDB2XgwIHmudKRVIFSPD6PgdrBPgQQ+EuAAOhfFmwhgAACcSvw8ccfm6Fy2muEhECsCugv7O7UqlUr91u2PRLQX/T0i5Q1gd+tdbk+sGLrP1jxxAOH/csoYHVAbFhT5Mq2Io1jiFh7OuuQT006tF1HBoRL1atXD5cl14/rStPvvZYB/X+1WPHf47L8h2PS6LzY7vW4bt0607NWe2tGm+69916xg5/ly5eXTz/9VJo1a+YUM3/+fBMg/f333+WLL76QO++80+8PPE7GXNzYMXCqHPvZm3khi7evI/kqlcrF2ge/VHbuY6I8j8F1YvOILrCoPTrtpD2lhw8fLhUqVLB3iS6g+OSTT8oLL7xgRp5MnDhRLr/88oC94ePxeXQaygYCCAQUIAAakIWdCCCAQOwL6Grl7qF19913n1lcRlfSJiEQiwJ2gEbrpqu+lyoVG7/oxqIVdcobgRnfi4y3Rh4fPxH4+iesnohL1v/51aKByN09YqNH6JIlS5wKt2/fXvQX93hMC745JhtXBcG3GjRpxGGp16ygNWQ1Jeaad9Lqpvriiy/KE088IWlp0feI3LJli5mzUBumQ9tnz56dqbe6/tFI97dp00Z0EcT33ntPnn/+edFgKckbgezeR61FojyP3ojmXimDBg2Sw4f//AOK9sB+//33M11ce0wPHTpU9D4PGzbMHNc/GPXq1csvL8+jHwdvEEgYAWbPTphbSUMQQCDZBC644AKzGILdbh3SE88rV9vt0FcNlG3YsEG0F4V71Xt3Hve2rmq/bds20SHWOmTJy6RDDtXWy7Rr1y7TPu3t5FVSM+394A4yelW2XY7WV38p0Hm1slv3RFnAw0sT29n9qnOWbd682RqObY3HzqWknyFdeEWfQV24KjtJ50/Todn2L6XZKSunz/3PxyJvWV/Bgp8Zr//flSKD/i1yMHCHxYzZc/S9O+CiUyDEYzqa5hOd+zNU2rk1XWZOiz64GKpML47psNqWLVuaeQft4Ge1atWiKvrll192nre+fftmCn7ahTVo0MAJcB87dkxGjx5tH+I1mwJe3EetQiI8j9mkzPXT9WekzjNup1dffdXeDPj69NNPS+HCf85nop0J7Okl7Mw8j7YErwgklgAB0MS6n7QGAQSSTCDj0B4dLuee7zC7HFOmTBHtTWR/ZRzCHKh8nWPJzq+vOkwvY/rqq6+cPHfddZdzWOuvf7XXOZtq164t9evXlxIlSpj5z958881MQTcN0Oj8p9qTsGrVqmaep+LFi0uNGjVML5ysBgN1kYkrrrjC9Ko588wz5bTTTpM6derITTfdFLBHgdOAIBsaQB08eLB07tzZLFalw7G0fSVLljQ9eQYMGGACTkFOd3Z/8sknjpv2+NWkwciePXua1U118R6dG1OvM3fuXOe87Gzs2LFDtH7Nmzc390KHzeoQWzXXXki6yIcGqoOl5557zqmzDjWzk/7i7v6c6HZuBvnsemTlNbsm9jXdz4GuSGunt956yyxyor259bOsi0XpM6G9v3Tuv3BO3377rZ+tBlHDJf0cPfbYY8619POun1G9rj4D2qMwkvkMNWCqK1e3bt1aSpcubT6XOn+hPsfaji5dupggUcZfNsPVL6ePf2atzfWp1fsz2rR1l8jz1sf6ZO7FpwNWcfHixc5+95BpZ2ccbHz2zhHZtyc85Cfjj8jBfeHz5VaT9Q9vau5egEq/Z37++edRVcH9s1KH7oZK7t5qb7zxhunNFio/x8ILeHUf9UqJ8DyGF4utHMuXL3f+WK0/e8LN9a3BT50b1E7ue6b7eB5tGV4RSCwBhsAn1v2kNQggkGQCGpjTX36uuuoqp+X2UPjKlSs7+7K6oavOajDFTjqxf7ikPTDd5/Tp0yfTKRpIsfPYPTZ1PqYHH3wwU17doT3ItHerLnqjwRUdPq1D//6/vbOBvqKs8/jzRwzf2CTdCtM1N7Xi7ApKZq0KGAqJLxBZBoniSy+rWYlyQEpNOZonNWoVW/VIuJQoKiAZrgSeTDkooLaKtGWsdowNM1HP+q4w+3wHn/G5c+feO/feuffO3P/nd87/P3PnPs8zz3yemXvvfOf3+z1f+cpXyjxEJQ5J8LnkkktCsXLZsmWhOJrYcGyjQqLOO++8MIzRf0tefvKI05+S5Wt23muvvTYUh/xySesrVqwwYrBx48ayt+XdqlnR9SfhWuFY1bx4JS47bhoLcdRM6PIQdKZj0I23bgDkJdyoSTyeNWuWkZdEkheuJhBwfdekAzNnzgwnFIh7dkocdX32+yKm8e3NepX67bdiPSsmrm/+dSDxWueu8pfp3IqbBGPlBpTYfeCBB5qlS5eaD37wg/Fi4Wt5GPtsq3lfirlmwtX4JQmrOp8kVsqbRsKscqfpGkmyDRs2hMKrvITjpv3oPNWfrh+dW+eff36473jZdr9+waamVdh7o/bE/xizwk4sPOqQRltovp7zONNnowTnotnzm7aYe25N573/2iuBWTznVTNpyi65OEw9GHSe0vrenT17thk7dmzZDNTVOqtwdvcgSeHverhUzfR54dLg6Hta+Sq1DWucQBbj6PZe9OvRHUeRlnqwfPPNN4cTHW23nU3WnMKUD9SZPjudcT06Eiwh0H0E8ADtvjHliCAAgV5GYNy4cWbixInRUb/44otVRbSoYE5WJIxceOGFJeKnfrzqRrKnp6eklxI9Nbv1nXfeGR6zE+Ykuu25554lZfVCIeGVRNWywnbDhAkTSsRP5VUbOXJkSQJ91Zs7d27ojaew+2qmfcuj1Rc/+/XrFwpYw4cPD73iXH2JVPJm1Y2zxK40JrHMFz/9OknCs/9+tXXdFKh/EqgcY5Xv27evGTx4cOgNqpt0Zwr51LEOGzbMbN682W0OlxpDjY/+4uPptrtlScWcvciSSdKhSUA56aSTIvFT14C8yiTyy5vS5y1PlcMOO6zi2Ce1n7RN+5SnswRQX/zUjeCQIUNCMdMXWXWOapzljR03hbrL89gXPwcMGBAeg7w+5b2qBzbOtG89pLjsssvcpo4tF91nzJvNRfqbBcu3zRrfiYMQc5emQx5N+owRX4Xzypt9zpw5Zu3atQ3lpWzX8dx23avm7TrG4P5fvmGe+WPtB3Lt6r/C3fXgQg8B9Bler61ZsyaKcJCntMawlslD29n69evdKssmCDQ7jtp1N1yPTSDsWFV9V+k33LnnnmtchEy1zkjk1G9EZ4pyccb16EiwhED3EUAA7b4x5YggAIFeSECh8L5QIe8weeUVwXRjLu8zmcTG+fPnG3mwSVzUD9S4gCnRREKRhFOF5sqbTCKdvGA2bdoU3oT6T/8lAOjHbBpTX2TKsaYZfOWht3z58rBdiQna7kzvT5s2zb0sWy5atCiaZVRvKmRcudrU10ceeSTMVSWxWn1TqL+zJUuWmB//+MfuZcWlPEwUDi173/veZ5QzTj/8Ffal0GkJr42awisfeuihqLqELE0mIK9P7Xf16tXhukLy/fNu5cqVoWgaVbQrOg/lRag/CarOdIPvtrulP26uXF6WWTJJOialc9C5L5PwKWFb58b1119vxFVh97o+nElokTjajGnyFI2hM6VkkIezxlkiq7yX5XWsbb54qbQHbqZqV3f69OlRGgeJtRJJ1Wcdg1JbyFtYr7Xd5V1TXQmgut47ZfZjxKx6vPm9v/B/9oHLu47YzTdYRwvO20xV5P2ph0RKnaCHFTpvTz/99CiFhV5LrM6T/eG/3jIP35fuoY/rt8at0mzxrky7lgcccECYL1czsqcRLpP65T9MkwCaxvwco4pOwJojkMU4qgdFvx6bo1ic2pdffnn04E8P0AcOHBh1nusxQsEKBLqOAAJo1w0pBwQBCPRGAhLAFArv2znnnBOKiP62PK5L/JIddNBB5he/+EV4w67jkSlvoGbr9EP8n3/++VBE1EQfEiU/+9nPRqHoCoHSTajCa32TgJTWRo0aZVatWmV0M+SbhAWJPgo5dyaPVOUNi5tCpSVGOlO/lJ9KQoS8KJ3J81Fil8TGY4891m0OBWGJr9VMIpVMuTMl/Co8WWkEFEapPjUqJkoQ0zg4k0eZxDDlpPOFK4lcxxxzTPieJv9wJoHLF0/d9iIv28lk9OjR4SzP8RQWuiaU2sDPDagcovKGbsQkuLoHD6qv3K7KYfjlL3+5RMSR16623X777dG5q/QLCvP1TekpnClM/owzzihpR+/Js1TbJdA5k1epf7657e1abnremM02BD4LW/9UFq3U34afu05itQRq3+vctahx0wMhXdN6SJYH27o1aFjI/MNjb5u1973R8cNQ3mU/fLaRDvn5tf2HDdXact+TKuPXr1aH9yoTyGIc1XqRr8fKdLrrHXl++r8T9bvRN/964nr0ybAOgeITePcurPjHwhFAAAIQ6NUEFHYnocJNNCPvSXmIyUMy77b77rsbeUxqwpUk03Hofd/kJbnTTjv5m6J1CQDyNnS5D9NOuCJRTx53El6TTBMsydtWYqg8UBU2fNFFF4XikF/+qquuKsn/dt1111XNQ6p2FaYqj1YJm/pT/7WtmsljTx6W8Ztv5f9s1BQO7ZuELoljlUweoPJslbeZeIiLbibk+deoCFtpX53a3i4mEscVRlvpvNb7Orckero0CbqJayTkVoK5m61aXHWM++67b0XEI0aMCNNOKAevbPHixUZpAdRXeYlqEiVn/sQSbpu/lAe3HhDoetOEWn74vV+uHetZiZ/qa5Zt1XPsvseZWErI0XjpT97lyg/5wAMPGE1qJ9PDlc9//vOhl7smu+qkPbDUhrJv2PYQrJF+3P7vr5rBn36P2f49PY1Uz00dfV87q/Q96N53S/+BlPuuc++x7ByBIlyPW+bNNMGqJRaSdaVOay9t8xzfuuAKs/U/q/82KW3SpsEZdoLp86XKETOl5Vv7St9XenDuJsn86Ec/auQw4BvXo0+DdQh0F4E+3XU4HA0EIACB3k1AE5X4IcnyGJPQkXc78cQTjR/OF+9vXJiRt2i1SSIkvElYceaLM25b0lLeadXEPtVRGPz48eOj6gsXLoyEVrdRIqmzT33qU6kEKk1ooTB2Z/LkqjXplISvatxcW2mXujFwaQBURzcJyutZyyR4TZo0KSomDxg/t1b0RgFX2snk5JNPLjlvk3Dttddexp8xXt7NLv9jUvlK25RqwZlyCUqUrGU6P+Xtq37OmDEjFOpVRw8wJLo502eOclBWMgm58lpWSLz632wof6X9pNn+duPaW1nz1sGyI+YLLkp/IQ9seS1rsirlX1U+PHnw6mGYPmdkEr/1eacHFp2yV1/eahbd+O4kJI304/lnt6aePKmR9ttVx/c4a0QA9SdzaVef2U8ygUJcj0/adD9Pr7N/NoIl7d8L70SlbN6Uvk7Y9joT/PHRZFht3qrvSkVZuNzp+t7Sg6H4Ncf12OaBYXcQaCMBBNA2wmZXEIAABFpNQCFx8jb0bcqUKWF+TH9b3tY1yUs180VdlZOnZC2TV6WztDeHEnbS2AknnBAVk4Dg51/TD2eJO840WU1a80PJJSC5H+mV6vuTYFQqU892f+Zw1fOFtlrt+OKtynaLANpOJhKk0thxxx0XFZNIrjy19Zgm6fAnTVGKiTTeupoYS2kgbrrpptBDWakdZPLk9FNDyENVkx7Ja9ufRMvvY9xr2X+vnesD/i67vWXZVj29UpoQ5bOT57s8Pf1cxX47eqDhP5y5//77yzzr/fKtXr/rP14zL7/UvAB79/zXzAvPZahkt/rAE9r3r4daD75cdZc+Rq8bzT3q2mKZHYEiXI99Ll5strv1f+v665n4nRBSz+SZddXTfvp8Z1t+6+wo19+Svvf0e2zdOiv8WlP0gh4EJn1ecj3Wz5caECgKAULgizJS9BMCEIBASgKa1VneePPmzQtrSJCTh5W8QfNq8lqqZn7eTJXzvTsr1ZMoU69p4pA0Fve6VIipZs2W+WKoXiuc3Y2FXlcz/UD3TW1VE3uzFkDjomVaHupzfEx+//vf+4dS2PV2MknLO37+pU3x4Abh6aefdqvhMt5eyZspX8iDVJMdOY9C5ROVp7SuQ3lrS3zTLPFKH5EnG7ibMTvYj4rX65uDJ/EQ/vFDiZtbvtHPC1trZ/K2V/oQ5TmWKVWF79Feq35W7296Zou5d9HrmTT3pm3mjhteNWfMSE5dkslOWtyI/8DOT01Rbbd+OU2yh+WDQBGuR+V2Nv0H1AWsp9+OYcB8zw47mZ4669a1oxYUVlSKcpYr6kAm8VO5p5VDPcm4HpOosA0C3UEAAbQ7xpGjgAAEIFBCQDe48gpzP/buueeecPblToaalnQw9qLem7d6y8d2l/hSP4jf//73J74X36gwZN980dNfVxl55Ma9cv261dbVloSjShZPDVCpXNrtmmDKt7SzEauORGyJXS43ZbcIoO1ionx+zqPSH4Ok9bhgWWvCrHgb8VnX4+dzvHya1xLWlH9SXsMut5rq6XyQF63+pk2bZjTbrjxYla9Ynt/hjXiaHbSozPb2l/DQjxmz8rHmdvCe7Y0Zsl9zbbSrtrxznQDqvKHatW+3nwXXvmLemf/ObWpq+dDyN80R494yHxlkB6KA5gsuaSMW/LyfygeNFZNAHq7HYpJL12s3eaCLRlAaEImffsRNvCWuxzgRXkOgewgQAt89Y8mRQAACEIgIDBgwoEx0Uz64uIdhVKHDKxIfO23xMPtq/YmXfe6556Liml07K6vl2ZdWMEvbHz/xv8RMly8wTX3NaO/3R7kzu8HaxUTCYFoxUA8A/BC9F198sS7Uf/vbtsksXKVaHtiuXK3lhAkTwgcv1dI+/PnPfw5ngVcZ5Zf100XUar9V748b3nzLY2wWj371O503v+MGWtCkH87kvd5uW7f6TfP4Q5VzxDban1uueTXyQG60jU7V+9CH3nUf3rhxY6pu+J+x+s7Hikmg09djMaml67UmaNQDNyd+KmpGD3+qiZ9qmesxHV9KQaCIBPAALeKo0WcIQAACKQjoR59yWrpZmxUKrxyDy5YtS1E7uYgLb01+d9vWtPnL/DbSCj9+nazX/aT3tdqu5hUY98T52te+Zhq9Oa2VGzVrbr4QJs89he/3758urFTnhi8EKx9tN1i7mIh1WtNEDv4kQ/WyjgvbmzdvTrvrmuUkbCq35IYNG8L8kvI+12vfK9Q1onyVQ4cONZpIrNYNqavTiqVC14882JjlaxprffddjRk/orG6najlexj653c7+rJlS2Bunf1KS3b19H+/bVYte8P8y+gdWtJ+KxsdNGhQ1HzaB5V+jui8pZaIDoaVmgQ6eT3W7FyBC3z3u981l156aXQE+j2l/NSatK+WcT3WIsT7ECguAQTQ4o4dPYcABCBQk8CPfvSj0CPLeYr86le/Mtdff72ZPHlyzbpJBdKIm/UIiUn76NQ2iUAK4ZUnYy3zbzxV1g9F33///Uuqjxs3rmoYe0nhDr+I5xTVcWqG9zSmMGw/J11cZEvTRh7LtIuJwtIlEqaZzCQukAwcOLAudP75qoryyszalLtWXuf60w3+r3/96zAPsfKE+p7N+mxS+Lw8p+O5frPuU7X2zhhrOfzVTt71p2qlyt9T/tDpdu60nXcsf68dW+69995whnedP0plMGvWrJq79fPafuxjNv6/jaa8n5ue2dqyPS60uUAPGtbP7LCjzXFYINPnjK59fQboetTnqe9RHz8UPTBxaUbkDX7ggQfGi/C6AwSKdj12AFFbdjl16lRz5ZVXRvvSd8zcuXONUs2kMa7HNJQoA4FiEkAALea40WsIQAACqQi4UHh/1mgJEiNGjEhVPy4GuvyO1Spn6U1WbT9ZvyfxUx6M1W463T7jAqg/UVFcAH3kkUcKK4Bqspy0AmicSdp8qo5pXpdxAbRVTORBK+HDP5cqMXnmmWdK3tp7771LXtd6ERdA4+1Vq3/xxReb3XbbzWjCpgMOOCAU3aqV13tKcTFmzJjwT/mJ5RWqSZOcJ7XOHQmkRx55ZK2mWva+coFecLoxVy8w5sFtkwTX3NffW89PiZ8f3qNm0ZYV0EOHn/zkJ2H7urm/7LLLzI47VlZjNXO4PHKdJc2A7N5rxVLPl447pXL/stjnXzduMf+wb7FucST+6/zXAwKZcngrT24l0/XiHkhK/Ewr7FRqj+3ZECja9ZjNUeerFX0G+uLnueeea6644orUKWZ0NFyP+RpTegOBLAkU69dBlkdOWxCAAAR6CYFjjz3WnHLKKeamm24Kj1ieIwqFT2NxbzTlGqw1YcratWtLmk4TNl9SoYMv7r777lTesUqq70wisT8DusQofzIghfmmNeWm0jipDU1AdPjhh4eTxqSt32y5uNin49T5k8bcjbsrWyt835XL+7KdTFauXJlKAPXPP3l/1Ssc7rzzzkZ5bF3+TTchTq2xkPj7ve99Lyp29dVXm2984xvmt7/9rdF1L89CiTLyPK9kStugib1UZtKkSVExhczXexxR5YxW5M059SRjVj1uzHybKWTju6l9S/awYz9jjjnUGOUO1Xonbfjw4dHnjcQXfX58/etfr9glTci2fv368H2FgipNSjtt5PjWip/tPJas9/XFL34xEkB/+MMfmokTJ1YUba666qpo96efbpV7LBcEinY95gJahp3QZ5v/HTV9+nTz/e9/v6E9cD02hI1KEMg9AQTQ3A8RHYQABCDQPAGJDQp/d6HwvgeQWq8kUsZzC0roqJZrTO0sXry4+Q53qIWf//znNQVQCTXz5s2LeijvWl8o3m677YxugsRbJlF1xYoVZuTIkVGdSivKWaUQOpmEIidUVCqf9XYl/ldIrAuRveGGG8IQZomx1UyT6viil4SVtF7G1drNw3vtZKJ8ZfL60jlUyTRBiiZ2cHbEEUeYXXe1roh1msbnlltuCWvJI/Phhx8O83FWa8YXuXV+Kr2DbP78+eYHP/hBuK4HAjNmzDC1PID9hwaqWM1rMWy4jf8+/c/G6O9Pm4xZt8GYzS/Z2ezfNmaAnWh7H5tt4J8+Yow8RvNgErMV3uk+k+TtpFmlBw8eXNY9jbPGxtmFF15o4jmL3Xss20/gC1/4gtGYyCNakQOnnXZaeK37nwf6jj377LPNfffdF3ZQ6S/0gLOTtuuXDjJv//XlTLrQZyf7FKLAxvXY2cE788wzo/zYetDm5wCtt2dFvR7rPU7KQ6DXEbBfpBgEIAABCBSEwC677BLYL6rwb/z48XX1+q677orqujbc0nqEJrb1u9/9rqSOFYMC6wWaWFYbr7nmmpLyav+nP/1pWXl7s15SzuYyKyvjb7A5BEvK2xAn/+3EdStCRnU+85nPJJaZM2dOVMaxuPzyyxPLaqP1bgs+97nPRXWs911gZ1EuK//4448HNoQqKmdneQ2s521ZOX/Dz372s6i8+mJ/vPtvh+vWe6ukjA1dLivT7AY7SVbJPnSe2Ql3KjZrUwcEOn8cPy3POuusxPJWfInKWdE4sUzWG604EO1TfdM5Xa+1ikn8OlD/NMaVTKzjx2MfOJQVt8JmyTGvW7eurIzNwxloDNy4HX300eH5XVbwnQ1/+ctfAitsR+UPPvjgqOiaNWui7WrPeoVG71VasWJ/SR0r+FQqyvYaBGze5cCm3oh4Wg/0wHo+Bb/5zW8CjfOSJUsC6xUaWNE6KmMfyAQ2pUmNlnm7GQJi764vmxM5VVO6nl0dLUeNGhXYB3Ph98ytt94aHH/88dH7Gs/bbrstVbsUapxAvePYrdfjlgVXBm8dvUOw5c7ZjcNsYc34byj9XrYP8VP/6fdr3Lge40R4DYHiE5DXDwYBCEAAAgUh0IwAqkOcPHlydPPk32RVEkBVx87qXFJnwoQJQVx4s7NSB9OmTSsp59ovmgCqfltvxkBik282t2lw1FFHlRzjt771Lb9Iyfq3v/3tkrI2t2NgQ5xLyuiFzckX3HjjjSWCqfWiC6wnaFnZdgig2qkv8oqHzgGbn7KsP9bzM5Bw5sZaS+stWlEkL6oA2iomSQKoxn7mzJll559uqq3HZQlriSFJlkYAVT2JZP7YWa/QwIbFlzUp0fiTn/xkVFZ9tB7OJeUk8vtt2VyhwWuvvVZSRi90Xem4rVdbVN56LJaVY0N9BCRy29yuEVN/LOLrU6ZMqSp217dnSlciUK9w5tpZsGBBYD0Ja46lDYN3VVi2kEAj49iN12PeBVAbjVPzmol/Fvqvbd7QxLOI6zERCxshUFgCOQngsR8/GAQgAAEItJyAZghWaLbCaNOaQor8PJYKd9XfkCFDjPUCCyduUTieZnqWWW9Lozyj1issfK1Q2SKYwoj1pzyHVrwMJxNR2PqgQYPC/IZKG+BP8KSJZBSuWMmUh8p63YazW6uMQueHDRsWMtOkFVYoDEMdFfLuQs5dW7NnzzYKbe6UKf+cxlyTQsm0rtB4K4KZT3ziE2HOQY3vgw8+aF56ycYHv2MK/7v55pvNe9/7Xrepa5btYKJQV01Sc8EFF4QhzVZ4DvPLKhxW558bD0FVnljrsdIUX4VDWw+y8NxUQ5pYxQqZYQi1xllmb+TN0qVLjT8BmsIK4/k6rXeaOfTQQ80rr7wS1rvooouM8k3ahwbhhElKE6HZ63UcatOZzhlNjIQ1R0CTGT3xxBNhKgpNAuJfl2pZeYk1adU555wT5pZsbm/UbiUBhd7qe0e5PZWawk125Pap712lnFAaCyyfBLge2z8u+vxrhXE9toIqbUKggwQKK93ScQhAAAK9kECzHqBCZvP4lT0lr+YBKg9FO1FGWR371VW2zQoggbxBtXTvz507t2yk4p5veQiBt4JSYIXhwN64RH13xxBf2uT4gRUYyo4rvuHll18OrIBcEnoab8t/rZB6e2MbbyZ63S4PUO3w2WefDRT+7vev2rq42ZylUV+TVorsAdoKJvHrQJ4m8qCtxlnvWXEyUEh6JUvrAar68iyt5Bke74e8Nq2AVuad6vqhcEE7G3XN/rt27Wzygc1L6aqzzIiAPrOfeuqpwOYfDvT5ayeoCt54442MWqeZdhKQF7V90BSmkrGTnwX2QVrF66+d/WJf6Ql0y/W4ZdG/bQuB/+UN6Q++y0pyPXbZgHI4vZJAH/sjFIMABCAAgV5EYMyYMebUU09NfcSa1MTeRBtNEKSJNfwJGVwjmlFaXkfyBG1kQhbXTqeXe+yxh7F588zYsWONFZvLumPD2I28M+XtlmbyEHm3qfzy5ctDz08rDpW1qQ2ayfurX/2qefLJJ83UqVMTy7R7oyaxueOOO8JxlzdS0rirT/Iq02zgq1evNh//+Mfb3c227q/VTHR9ybPWpiAwOnfipomD5PUpL0pdc1lY//79jU1TYRYuXGjktVRpnOWR/Oijjxp5wlby6tZ1Y4U3o4l4kvrv+iuO3/zmN4198GFsjkO3mWVGBPSZLQ9zTQJic8aGk1vJAxQrHgF9ZxxyyCHGPqQwo0ePDj3CK11/xTu63tHjbrkee4462fT511mmZ8SJvWPgEo6S6zEBCpsgUDACPZJ9C9ZnugsBCEAAAh0koFB3CSGaEV4i4T777BOGvlYSTjrY1aZ2rVBkHaMEKYUbK/zbeqw13aZC4RUCrDQEe+21V3hDK2ErSXBtamcZV7YeZOGs9HaCpzDMWUwkCCtkurdas0zspA1m0qRJET4JgnYym/C1wl51nVnvvfC822+//UKxudXX2euvvx6O82OPPRaG40v8lrBd74MN6ykTppNQ2Lv+9PoDH/hAeL4PHTrUSBTAIAABCEAAAhCAAAQg0C4CCKDtIs1+IAABCEAAAhCAgEegmgDqFWMVAhCAAAQgAAEIQAACEGiSAI/fmwRIdQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCC/BBBA8zs29AwCEIAABCAAAQhAAAIQgAAEIAABCEAAAhBokgACaJMAqQ4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQjklwACaH7Hhp5BAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACTRJAAG0SINUhAAEIQAACEIAABCAAAQhAAAIQgAAEIACB/BLom9+u0TMIQAACEIAABCDQvQT69u1r+vfvHx1gT09PtM4KBCAAAQhAAAIQgAAEIJAdgZ7AWnbN0RIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgfwQIAQ+P2NBTyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGMCSCAZgyU5iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH8EEAAzc9Y0BMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgYwIIoBkDpTkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgPwQQQPMzFvQEAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyJgAAmjGQGkOAhCAAAQgAAEIQAACEIAABCAAAdtIpwcAAAThSURBVAhAAAIQyA8BBND8jAU9gQAEIAABCEAAAhCAAAQgAAEIQAACEIAABDImgACaMVCagwAEIAABCEAAAhCAAAQgAAEIQAACEIAABPJDAAE0P2NBTyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGMCSCAZgyU5iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH8EEAAzc9Y0BMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgYwIIoBkDpTkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgPwQQQPMzFvQEAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyJgAAmjGQGkOAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyA8BBND8jAU9gQAEIAABCEAAAhCAAAQgAAEIQAACEIAABDImgACaMVCagwAEIAABCEAAAhCAAAQgAAEIQAACEIAABPJDAAE0P2NBTyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGMCSCAZgyU5iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH8EEAAzc9Y0BMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgYwIIoBkDpTkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgPwQQQPMzFvQEAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyJgAAmjGQGkOAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyA8BBND8jAU9gQAEIAABCEAAAhCAAAQgAAEIQAACEIAABDImgACaMVCagwAEIAABCEAAAhCAAAQgAAEIQAACEIAABPJDAAE0P2NBTyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGMCSCAZgyU5iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH8EEAAzc9Y0BMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgYwIIoBkDpTkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgPwQQQPMzFvQEAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyJgAAmjGQGkOAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyA8BBND8jAU9gQAEIAABCEAAAhCAAAQgAAEIQAACEIAABDImgACaMVCagwAEIAABCEAAAhCAAAQgAAEIQAACEIAABPJDAAE0P2NBTyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGMCSCAZgyU5iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH8EEAAzc9Y0BMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgYwIIoBkDpTkIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgPwQQQPMzFvQEAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyJgAAmjGQGkOAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQyA8BBND8jAU9gQAEIAABCEAAAhCAAAQgAAEIQAACEIAABDImgACaMVCagwAEIAABCEAAAhCAAAQgAAEIQAACEIAABPJDAAE0P2NBTyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGMCSCAZgyU5iAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH8EEAAzc9Y0BMIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgYwL/D2GXtqnL63OzAAAAAElFTkSuQmCC" width="672" /></p>
<pre class="r"><code>ggsave(&quot;fig_A6.png&quot;, topic_eval_plot, width = 9, height = 11, dpi = 300)
ggsave(&quot;fig_A6.pdf&quot;, topic_eval_plot, width = 9, height = 11)
ggsave(&quot;fig_A6.eps&quot;, topic_eval_plot, width = 9, height = 11)</code></pre>
<pre><code>## Warning in grid.Call.graphics(C_lines, x$x, x$y, index, x$arrow):
## semi-transparency is not supported on this device: reported only once per page</code></pre>
<pre class="r"><code># Prepare dfm for topic model, set metadata

dfm_abs_md &lt;- quanteda::convert(dfm_abs, to = &quot;stm&quot;)
dfm_abs_md$meta &lt;- docvars(dfm_abs)

# Check number of documents
ndoc(dfm_abs)</code></pre>
<pre><code>## [1] 40468</code></pre>
<pre class="r"><code># Run STM model with 10 topics (will take time to run)

# Run model
stm_md_10 &lt;- stm(
    documents = dfm_abs_md$documents,
    vocab = dfm_abs_md$vocab,
    K = 10, # Number of topics
    data = dfm_abs_md$meta,
    seed = 123,
    verbose = TRUE
)</code></pre>
<pre><code>## Beginning Spectral Initialization 
##   Calculating the gram matrix...
##   Using only 10000 most frequent terms during initialization...
##   Finding anchor words...
##      ..........
##   Recovering initialization...
##      ....................................................................................................
## Initialization complete.
## ....................................................................................................
## Completed E-Step (5 seconds). 
## Completed M-Step. 
## Completing Iteration 1 (approx. per word bound = -8.348) 
## ....................................................................................................
## Completed E-Step (5 seconds). 
## Completed M-Step. 
## Completing Iteration 2 (approx. per word bound = -7.966, relative change = 4.570e-02) 
## ....................................................................................................
## Completed E-Step (5 seconds). 
## Completed M-Step. 
## Completing Iteration 3 (approx. per word bound = -7.918, relative change = 6.027e-03) 
## ....................................................................................................
## Completed E-Step (5 seconds). 
## Completed M-Step. 
## Completing Iteration 4 (approx. per word bound = -7.899, relative change = 2.408e-03) 
## ....................................................................................................
## Completed E-Step (4 seconds). 
## Completed M-Step. 
## Completing Iteration 5 (approx. per word bound = -7.890, relative change = 1.133e-03) 
## Topic 1: green, china, environmental, carbon, development 
##  Topic 2: countries, energy, emissions, co2_emissions, global 
##  Topic 3: energy, policy, market, policies, renewable_energy 
##  Topic 4: production, biomass, potential, oil, carbon 
##  Topic 5: sustainability, water, social, sustainable, research 
##  Topic 6: energy, building, households, household, buildings 
##  Topic 7: environmental, waste, public, social, sustainable 
##  Topic 8: china, environmental, emissions, industrial, water 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, technologies 
## ....................................................................................................
## Completed E-Step (4 seconds). 
## Completed M-Step. 
## Completing Iteration 6 (approx. per word bound = -7.885, relative change = 6.106e-04) 
## ....................................................................................................
## Completed E-Step (4 seconds). 
## Completed M-Step. 
## Completing Iteration 7 (approx. per word bound = -7.882, relative change = 3.711e-04) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 8 (approx. per word bound = -7.880, relative change = 2.527e-04) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 9 (approx. per word bound = -7.879, relative change = 1.864e-04) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 10 (approx. per word bound = -7.878, relative change = 1.428e-04) 
## Topic 1: green, china, environmental, carbon, development 
##  Topic 2: countries, energy, co2_emissions, emissions, global 
##  Topic 3: energy, policy, policies, market, renewable_energy 
##  Topic 4: production, biomass, potential, oil, forest 
##  Topic 5: sustainability, sustainable, research, social, framework 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, waste, public, factors, social 
##  Topic 8: water, emissions, china, industrial, environmental 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 11 (approx. per word bound = -7.877, relative change = 1.139e-04) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 12 (approx. per word bound = -7.876, relative change = 9.425e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 13 (approx. per word bound = -7.875, relative change = 8.123e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 14 (approx. per word bound = -7.875, relative change = 7.119e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 15 (approx. per word bound = -7.874, relative change = 6.325e-05) 
## Topic 1: green, china, carbon, environmental, development 
##  Topic 2: energy, countries, co2_emissions, emissions, energy_consumption 
##  Topic 3: energy, policy, policies, development, market 
##  Topic 4: production, biomass, potential, oil, agricultural 
##  Topic 5: sustainability, sustainable, research, social, framework 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, waste, public, factors, social 
##  Topic 8: water, emissions, china, urban, spatial 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 16 (approx. per word bound = -7.874, relative change = 5.743e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 17 (approx. per word bound = -7.874, relative change = 5.300e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 18 (approx. per word bound = -7.873, relative change = 4.958e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 19 (approx. per word bound = -7.873, relative change = 4.578e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 20 (approx. per word bound = -7.872, relative change = 4.148e-05) 
## Topic 1: green, china, carbon, environmental, development 
##  Topic 2: energy, countries, co2_emissions, emissions, energy_consumption 
##  Topic 3: energy, policy, policies, development, local 
##  Topic 4: production, biomass, potential, oil, process 
##  Topic 5: sustainability, sustainable, research, framework, social 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, waste, public, factors, social 
##  Topic 8: water, urban, emissions, china, spatial 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (4 seconds). 
## Completed M-Step. 
## Completing Iteration 21 (approx. per word bound = -7.872, relative change = 3.802e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 22 (approx. per word bound = -7.872, relative change = 3.609e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 23 (approx. per word bound = -7.872, relative change = 3.442e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 24 (approx. per word bound = -7.871, relative change = 3.154e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 25 (approx. per word bound = -7.871, relative change = 3.066e-05) 
## Topic 1: china, green, carbon, environmental, policy 
##  Topic 2: energy, countries, emissions, co2_emissions, energy_consumption 
##  Topic 3: energy, policy, development, policies, local 
##  Topic 4: production, biomass, potential, oil, process 
##  Topic 5: sustainability, sustainable, research, framework, environmental 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, waste, public, factors, social 
##  Topic 8: water, urban, spatial, emissions, china 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 26 (approx. per word bound = -7.871, relative change = 2.893e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 27 (approx. per word bound = -7.871, relative change = 2.576e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 28 (approx. per word bound = -7.871, relative change = 2.366e-05) 
## ....................................................................................................
## Completed E-Step (46 seconds). 
## Completed M-Step. 
## Completing Iteration 29 (approx. per word bound = -7.870, relative change = 2.201e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 30 (approx. per word bound = -7.870, relative change = 2.082e-05) 
## Topic 1: china, green, carbon, environmental, policy 
##  Topic 2: energy, countries, emissions, co2_emissions, energy_consumption 
##  Topic 3: energy, policy, development, policies, local 
##  Topic 4: production, biomass, potential, oil, process 
##  Topic 5: sustainability, sustainable, research, environmental, framework 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, waste, public, factors, social 
##  Topic 8: water, urban, spatial, land, cities 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 31 (approx. per word bound = -7.870, relative change = 1.954e-05) 
## ....................................................................................................
## Completed E-Step (69 seconds). 
## Completed M-Step. 
## Completing Iteration 32 (approx. per word bound = -7.870, relative change = 1.826e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 33 (approx. per word bound = -7.870, relative change = 1.696e-05) 
## ....................................................................................................
## Completed E-Step (112 seconds). 
## Completed M-Step. 
## Completing Iteration 34 (approx. per word bound = -7.870, relative change = 1.615e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 35 (approx. per word bound = -7.869, relative change = 1.593e-05) 
## Topic 1: china, green, carbon, environmental, policy 
##  Topic 2: energy, countries, emissions, co2_emissions, energy_consumption 
##  Topic 3: energy, policy, development, local, policies 
##  Topic 4: production, biomass, process, potential, oil 
##  Topic 5: sustainability, sustainable, environmental, research, framework 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, public, waste, social, factors 
##  Topic 8: water, urban, spatial, land, cities 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (38 seconds). 
## Completed M-Step. 
## Completing Iteration 36 (approx. per word bound = -7.869, relative change = 1.572e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 37 (approx. per word bound = -7.869, relative change = 1.584e-05) 
## ....................................................................................................
## Completed E-Step (38 seconds). 
## Completed M-Step. 
## Completing Iteration 38 (approx. per word bound = -7.869, relative change = 1.567e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 39 (approx. per word bound = -7.869, relative change = 1.579e-05) 
## ....................................................................................................
## Completed E-Step (239 seconds). 
## Completed M-Step. 
## Completing Iteration 40 (approx. per word bound = -7.869, relative change = 1.637e-05) 
## Topic 1: china, green, carbon, environmental, policy 
##  Topic 2: energy, countries, emissions, co2_emissions, energy_consumption 
##  Topic 3: energy, policy, development, local, policies 
##  Topic 4: production, biomass, process, oil, potential 
##  Topic 5: sustainability, sustainable, environmental, framework, research 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, social, public, factors, waste 
##  Topic 8: water, urban, land, spatial, cities 
##  Topic 9: system, energy, power, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 41 (approx. per word bound = -7.869, relative change = 1.700e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 42 (approx. per word bound = -7.869, relative change = 1.732e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 43 (approx. per word bound = -7.868, relative change = 1.679e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 44 (approx. per word bound = -7.868, relative change = 1.657e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 45 (approx. per word bound = -7.868, relative change = 1.569e-05) 
## Topic 1: china, green, carbon, environmental, policy 
##  Topic 2: energy, countries, emissions, co2_emissions, energy_consumption 
##  Topic 3: energy, policy, development, local, policies 
##  Topic 4: production, biomass, process, oil, potential 
##  Topic 5: sustainability, sustainable, environmental, framework, research 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, social, public, factors, consumers 
##  Topic 8: water, urban, land, cities, spatial 
##  Topic 9: system, power, energy, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 46 (approx. per word bound = -7.868, relative change = 1.513e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 47 (approx. per word bound = -7.868, relative change = 1.521e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 48 (approx. per word bound = -7.868, relative change = 1.459e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 49 (approx. per word bound = -7.868, relative change = 1.400e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 50 (approx. per word bound = -7.868, relative change = 1.348e-05) 
## Topic 1: china, carbon, green, environmental, policy 
##  Topic 2: energy, countries, co2_emissions, emissions, energy_consumption 
##  Topic 3: energy, policy, development, local, policies 
##  Topic 4: production, biomass, process, oil, waste 
##  Topic 5: sustainability, sustainable, environmental, framework, research 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, social, public, factors, consumers 
##  Topic 8: water, urban, land, cities, spatial 
##  Topic 9: system, power, energy, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 51 (approx. per word bound = -7.868, relative change = 1.325e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 52 (approx. per word bound = -7.867, relative change = 1.378e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 53 (approx. per word bound = -7.867, relative change = 1.392e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 54 (approx. per word bound = -7.867, relative change = 1.349e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 55 (approx. per word bound = -7.867, relative change = 1.302e-05) 
## Topic 1: china, carbon, green, environmental, policy 
##  Topic 2: energy, countries, co2_emissions, emissions, energy_consumption 
##  Topic 3: energy, policy, development, local, research 
##  Topic 4: production, biomass, waste, process, oil 
##  Topic 5: sustainability, sustainable, environmental, framework, research 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, social, public, factors, consumers 
##  Topic 8: water, urban, land, cities, spatial 
##  Topic 9: system, power, energy, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (4 seconds). 
## Completed M-Step. 
## Completing Iteration 56 (approx. per word bound = -7.867, relative change = 1.235e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 57 (approx. per word bound = -7.867, relative change = 1.158e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 58 (approx. per word bound = -7.867, relative change = 1.077e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 59 (approx. per word bound = -7.867, relative change = 1.037e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 60 (approx. per word bound = -7.867, relative change = 1.081e-05) 
## Topic 1: china, carbon, green, policy, environmental 
##  Topic 2: energy, countries, co2_emissions, emissions, energy_consumption 
##  Topic 3: energy, policy, development, local, research 
##  Topic 4: production, biomass, waste, process, oil 
##  Topic 5: sustainability, sustainable, environmental, framework, research 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, social, public, factors, consumers 
##  Topic 8: water, urban, land, cities, spatial 
##  Topic 9: system, power, energy, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 61 (approx. per word bound = -7.867, relative change = 1.154e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 62 (approx. per word bound = -7.866, relative change = 1.214e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 63 (approx. per word bound = -7.866, relative change = 1.216e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 64 (approx. per word bound = -7.866, relative change = 1.161e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 65 (approx. per word bound = -7.866, relative change = 1.091e-05) 
## Topic 1: china, carbon, green, policy, environmental 
##  Topic 2: energy, countries, co2_emissions, energy_consumption, emissions 
##  Topic 3: energy, policy, development, local, research 
##  Topic 4: production, biomass, waste, oil, process 
##  Topic 5: sustainability, sustainable, environmental, framework, research 
##  Topic 6: energy, building, households, buildings, household 
##  Topic 7: environmental, social, public, factors, consumers 
##  Topic 8: water, urban, land, cities, spatial 
##  Topic 9: system, power, energy, optimal, optimization 
##  Topic 10: electricity, energy, cost, costs, scenarios 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 66 (approx. per word bound = -7.866, relative change = 1.100e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Completing Iteration 67 (approx. per word bound = -7.866, relative change = 1.079e-05) 
## ....................................................................................................
## Completed E-Step (3 seconds). 
## Completed M-Step. 
## Model Converged</code></pre>
<pre class="r"><code># inspect words for each document
plot(stm_md_10, n = 6)</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre class="r"><code># transform to data frame
doc_topic_df &lt;- make.dt(stm_md_10, meta = dfm_abs_md$meta)

# get averages of each topic
dtp &lt;- doc_topic_df |&gt;
    select(starts_with(&quot;Topic&quot;)) |&gt;
    colMeans()

# 2.1 Topic topwords and labels

topic_topwords &lt;- stm_md_10 |&gt;
    labelTopics(
        topics = NULL,
        n = stm_md_10$settings$dim$K,
        frexweight = .5
    )

# Topwords Frex
topwords_frex &lt;- topic_topwords$frex |&gt;
    t() |&gt;
    as.data.frame() |&gt;
    as.list() |&gt;
    lapply(paste, collapse = &quot;, &quot;)

names(topwords_frex) &lt;- NULL

# Topwords Highest prob
topwords_hp &lt;- topic_topwords$prob |&gt;
    t() |&gt;
    as.data.frame() |&gt;
    as.list() |&gt;
    lapply(paste, collapse = &quot;, &quot;)

names(topwords_hp) &lt;- NULL

df_f &lt;- data.frame(topic = 1:10, Topwords = paste(&quot;(FREX)&quot;, unlist(topwords_frex)))
df_h &lt;- data.frame(topic = 1:10, Topwords = paste(&quot;(Highest)&quot;, unlist(topwords_hp)))

# print for manual labeling
df_h</code></pre>
<pre><code>##    topic
## 1      1
## 2      2
## 3      3
## 4      4
## 5      5
## 6      6
## 7      7
## 8      8
## 9      9
## 10    10
##                                                                                                                               Topwords
## 1                  (Highest) china, carbon, green, policy, development, environmental, china&#39;s, industry, efficiency, carbon_emissions
## 2        (Highest) energy, countries, co2_emissions, energy_consumption, emissions, effects, consumption, economic_growth, oil, growth
## 3                                 (Highest) energy, policy, development, local, research, policies, new, political, social, transition
## 4                                          (Highest) production, biomass, waste, oil, process, potential, co2, fuel, biogas, bioenergy
## 5  (Highest) sustainability, sustainable, environmental, framework, research, assessment, performance, process, management, indicators
## 6                   (Highest) energy, building, households, buildings, household, solar, energy_efficiency, residential, heat, heating
## 7                           (Highest) environmental, social, public, factors, consumers, adoption, companies, practices, green, survey
## 8                                              (Highest) water, urban, land, cities, spatial, areas, area, emissions, climate, regions
## 9                                   (Highest) system, power, energy, optimal, optimization, network, operation, systems, load, control
## 10                          (Highest) electricity, energy, cost, costs, scenarios, technologies, renewable, power, potential, scenario</code></pre>
<pre class="r"><code>topic_labs &lt;- data.frame(
    topic = paste0(&quot;Topic&quot;, 1:10),
    lab = c(
        &quot;Emissions&quot;,
        &quot;Economy&quot;,
        &quot;Implementation&quot;,
        &quot;Biomass&quot;,
        &quot;Sustainability&quot;,
        &quot;Household Energy&quot;,
        &quot;Consumers&quot;,
        &quot;Ecology&quot;,
        &quot;Energy Systems&quot;,
        &quot;Renewable Energy&quot;
    )
)

df_h &lt;- tibble(
    Topic = topic_labs$lab,
    `Topic prevalence` = round(dtp, 4),
    Topwords = unlist(topwords_hp)
) |&gt;
    arrange(-dtp)

df_h_print &lt;- df_h |&gt;
    mutate(`Topic prevalence` = scales::percent(`Topic prevalence`, accuracy = 0.1)) |&gt;
    rename(`Words with highest probabilities` = Topwords)

# Table 2
print(
    xtable(df_h_print,
        label = &quot;tab:topwords&quot;,
        align = c(
            &quot;p{0.05\\textwidth}&quot;,
            &quot;p{0.2\\textwidth}&quot;,
            &quot;p{0.15\\textwidth}&quot;,
            &quot;p{0.5\\textwidth}&quot;
        ),
        caption = &quot;STM topic labels and associated topwords in policy-relevant article abstracts&quot;
    ),
    type = &quot;latex&quot;,
    table.placement = &quot;!h&quot;,
    caption.placement = &quot;top&quot;,
    file = &quot;tab_02.tex&quot;,
    size = &quot;footnotesize&quot;,
    include.rownames = FALSE
)

print(
    xtable(df_h_print,
        caption = &quot;STM topic labels and associated topwords in policy-relevant article abstracts&quot;
    ),
    type = &quot;html&quot;,
    table.placement = &quot;!h&quot;,
    caption.placement = &quot;top&quot;,
    file = &quot;tab_02.html&quot;,
    size = &quot;footnotesize&quot;,
    include.rownames = FALSE
)

lab_order &lt;- df_h |&gt;
    arrange(-`Topic prevalence`) |&gt;
    pull(Topic)

# Top Documents
docs &lt;- findThoughts(stm_md_10,
    n = 25,
    text = abs_ps$abstract_text
)

# Get doc DOIs
docs_df &lt;- docs$docs |&gt;
    as.data.frame() |&gt;
    t() |&gt;
    as.data.frame()
docs_df$topic &lt;- gsub(&quot;\\.&quot;, &quot;&quot;, rownames(docs_df))
rownames(docs_df) &lt;- NULL

docs_df &lt;- docs_df |&gt;
    left_join(topic_labs, by = &quot;topic&quot;)

docs_df &lt;- docs_df |&gt;
    select(topic, lab, everything())

# docs_df &lt;- docs_df |&gt;
#   select(topic, lab, doc1 = V1, doc2 = V2, doc3 = V3)

# Get docs index
index_df &lt;- docs$index |&gt;
    as.data.frame() |&gt;
    t() |&gt;
    as.data.frame()
index_df$topic &lt;- gsub(&quot;\\.&quot;, &quot;&quot;, rownames(index_df))
rownames(index_df) &lt;- NULL

index_df &lt;- index_df |&gt;
    left_join(topic_labs, by = &quot;topic&quot;)

index_df &lt;- index_df |&gt;
    select(topic, lab, everything()) |&gt;
    pivot_longer(starts_with(&quot;V&quot;), names_to = &quot;doc&quot;, values_to = &quot;index&quot;)

index_df$scopus_id &lt;- abs_ps$article_scopus_id[index_df$index]
index_df$abstract_text &lt;- abs_ps$abstract_text[index_df$index]
index_df$article_doi &lt;- abs_ps$article_doi[index_df$index]

# save abstracts with high topic proportions for manual validation
write.csv(index_df, &quot;data_dontshare/topdocs_expanded.csv&quot;, row.names = FALSE)

topdocs &lt;- index_df |&gt;
    mutate(
        topic = str_replace_all(topic, &quot;Topic&quot;, &quot;Topic &quot;),
        doc = str_replace_all(doc, &quot;doc&quot;, &quot;Doc &quot;)
    ) |&gt;
    mutate(
        topic_lab = paste0(topic, &quot;: &quot;, lab),
        doc_lab = paste0(doc, &quot;: &quot;, article_doi),
        text = paste0(&quot;\n\n&quot;, topic_lab, &quot;\n&quot;, doc_lab, &quot;\n&quot;, abstract_text)
    )

topdocs |&gt;
    # filter(topic == &#39;Topic 1&#39;) |&gt;
    pull(text) |&gt;
    cat()</code></pre>
<pre><code>## 
## 
## Topic 1: Emissions
## V1: 10.1016/j.jclepro.2023.136573
## Green credit policy is a policy enacted by the government to induce enterprises to balance environmental management and green economic development, but how green credit policy affects the green productivity of enterprises in practical situations is a topic worth exploring. This paper uses a double difference model to investigate the impact of green credit policies on green productivity of corporate from 2007 to 2020. It is found that the implementation of the Green Credit Guidelines can significantly improve the green production efficiency of heavy polluting enterprises. The implementation of the Green Credit Guidelines has a greater effect on the green production efficiency of enterprises among enterprises with smaller commercial credit financing scale and enterprises with lower capital use efficiency than other enterprises. The impact of the Green Credit Guidelines on state-owned enterprises, large-scale enterprises, and enterprises in the central and western regions is more obvious. And the implementation of the Green Credit Guidelines can affect the green production efficiency of enterprises through the financing scale effect and financing cost effect. This study provides insights for the government to further standardize the green credit system and ensure the effectiveness of green credit policy implementation, and provides ideas to promote green development of enterprises by analyzing the influencing factors of their green production efficiency. 
## 
## Topic 1: Emissions
## V2: 10.1016/j.enpol.2023.113803
## The carbon market policy is an effective means to promote green and low-carbon development, and is important for China to achieve the carbon peaking and carbon neutrality goals. We manually collated a list of non-pilot cities, neighbouring the regions implementing carbon market policies. With the panel data from 2006 to 2017, we examined the impact and mechanisms of the policy on non-pilot cities. The study found that the policy of the pilot city not only worked in its region but also promoted green and low-carbon development in the neighbouring non-pilot regions. This kind of external effect is self-reinforcing. Policy spillover effects and industrial relocation are important mechanisms for carbon market policies to exert externalities. However, the “pollution refuge hypothesis” holds if polluting industries dominate neighbouring areas. Further analysis shows that carbon market policies are more likely to have a point-to-surface effect in neighbouring cities if officials of the pilot city are at a critical stage. Such effect is strongest in Beijing, followed by Shanghai, while it is not effective in Hubei and Guangdong. Moreover, we found that carbon market policies were with synergistic effects in terms of carbon and pollution reduction. 
## 
## Topic 1: Emissions
## V3: 10.1016/j.spc.2022.01.017
## The impact of environmental regulation on enterprise green innovation has not yet formed a unified conclusion. Based on the sample data of Shanghai and Shenzhen A-share listed companies from 2010 to 2019, this paper takes the promulgation of the new Environmental Protection Law (NEPL) in 2014 as the exogenous shock of a quasi-natural experiment, and applies the propensity score matching and difference-in-difference (PSM-DID) model to test the influence of environmental regulation on enterprise green innovation. On this basis, we further analyze the heterogeneity according to the different nature of corporate equity and probe the mechanism of environmental responsibility and government subsidies in the impact of environmental regulation on green innovation. The results indicate that: first, the NEPL has significantly promoted the green innovation of heavily polluting enterprises, and the marginal effects of NEPL exhibit a fluctuating trend of &quot;first decline, then rise and then decline&quot; over time, and the overall trend is downward. Second, compared with non-state-owned enterprises, the NEPL plays a more significant role in promoting the green innovation of state-owned enterprises. Third, the environmental responsibility plays a mediating role in the impact of environmental regulation on enterprise green innovation. Government subsidies play a positive moderating role in the impact of environmental regulation on enterprise green innovation. This study not only provides empirical evidence for the &quot;Porter Hypothesis&quot;, but also enriches the research on the effect evaluation of environmental regulation policies, and also furnishes a reference for the construction of the green innovation system of enterprises. 
## 
## Topic 1: Emissions
## V4: 10.1016/j.jclepro.2020.120079
## There is currently no consensus on the relationship between environmental regulation and enterprise innovation. Moreover, empirical studies on this topic have generally been based on government indirect environmental regulation data. To examine this relationship further, this study uses government direct environmental regulation (GDER) data. Here, enterprise innovation is measured by innovation investment. Additionally, innovation investment is divided into two stages, a research stage and a development stage, to explore the effect of GDER on heterogeneous innovation investment. Furthermore, this study examines the impact of executive compensation on GDER and innovation investment, specifically looking at the two stages of innovation investment. The results indicate that GDER has a negative impact on heterogeneous innovation investment and executive compensation has different moderating effects: strengthening the negative relationship between GDER and research investment, while weakening the negative relationship between GDER and development investment. This study offers new insights into the relationship between environmental regulation and enterprise innovation considering the impact of executive compensation. 
## 
## Topic 1: Emissions
## V5: 10.1016/j.enpol.2022.113081
## Based on panel data of 114 prefecture-level resource-based cities in China from 2004 to 2018, this paper uses SBM model and super-efficiency SBM model to measure the energy efficiency of resource-based cities in China, and systematically analyzes the influence of Civilized City Policy on energy efficiency and the mechanism for that influence using difference-in-differences (DID) model and propensity score matching method. The findings show that the spatial distribution of energy efficiency of resource-based cities is higher in eastern China and lower in western China, and the energy efficiency was continually improved, with fluctuations, between 2004 and 2018. DID analysis and robustness analysis prove that Civilized City Policy is significantly conducive to improving energy efficiency of resource-based cities in China. This positive influence can also be achieved through the mechanism of technological innovation. In addition, there exists regional and type heterogeneity in the influence of Civilized City Policy on energy efficiency of resource-based cities. 
## 
## Topic 1: Emissions
## V6: 10.1016/j.energy.2021.122704
## With a sample of the listed new energy (NE) enterprises in China (2010–2017), this paper investigates the impact of venture capital on NE enterprises innovation by applying the Propensity Score Matching (PSM) model and Poisson model. The results indicate that venture capital has a positive and significant impact on the innovation of NE enterprises, among which the innovation incentive effect of government participation and joint venture capital is more significant. Compared with non-government participation venture capital, single venture capital and non-local investment, government participation venture capital, joint venture capital and local investment are more conducive to promoting the increase of patent applications of NE enterprises. Besides, local investment conducted by government participation venture capital and joint venture capital have a more significant innovation incentive effect of NE enterprises. This paper not only reveals the impact of venture capital on the innovation of NE enterprises, but also provides the empirical basis for the government to adopt the market instrument to promote the development of NE industry. 
## 
## Topic 1: Emissions
## V7: 10.1016/j.jclepro.2022.132061
## While the advancing process of achieving the carbon-neutral goal, how green credit policies guide enterprises to green production and low-carbon technology innovation has received much attention Based on the microdata of Chinese enterprises from 2004 to 2019, this paper constructs a difference-in-differences (DID) model according to the Green Credit Guidelines to investigate the impact of green credit policies on enterprises&#39; low-carbon technological innovation and its influence mechanism. The findings include that (1) green credit policy can significantly promote low-carbon technology innovation; (2) green credit policy has a more significant promotion effect on the low-carbon technology innovation of state-owned enterprises and ESG-certified companies; (3) mechanism analysis shows that green credit policies promote enterprises&#39; low-carbon technology innovation by increasing their R&amp;D investment and management efficiency. This study helps policymakers to better understand the impact of green credit policies on low-carbon technology innovation and provides evidence to support the promotion of energy conservation and emission reduction in developing countries. 
## 
## Topic 1: Emissions
## V8: 10.1016/j.jclepro.2022.134444
## Environmental courts play an important role in pollution control. It is unclear whether environmental courts could help to achieve China&#39;s carbon peak and carbon neutrality goals. This study investigates the impact of environmental courts on carbon intensity, using the data of 281 cities of China during 2003–2017. Using the multi-period difference-in-differences (DID) model, this study first evaluates the impact of environmental courts on carbon intensity taking the establishment of environmental courts as a quasi-natural experiment. Then, this study examines the influence mechanisms of environmental court on carbon intensity from the perspective of administrative enforcement, green innovation ability and cooperative effect of pollution control. Finally, this study analyses the heterogeneity test from the perspective of public participation, whether it is a low-carbon pilot city and geographical location. The main results are: (1) Environmental courts could reduce carbon intensity, which is supported by a series of robustness tests. (2) Environmental courts could reduce carbon intensity through improving administrative enforcement of local government and green innovation ability of firms and playing the cooperative effect in pollution control. (3) Heterogeneity test results show that in cities with high public participation, environmental courts could reduce carbon intensity more effectively. Compared with non-low-carbon pilot cities, environmental courts have a bigger role in reducing carbon intensity in low-carbon pilot cities. Compared with the middle cities of China, environmental courts also have a bigger role in reducing carbon intensity in eastern and western cities. This study provides new insights for China to reduce carbon intensity from the perspective of the establishment and implementation of environmental courts, and policy implications of how to effectively play the role of environmental courts in achieving carbon peaks and carbon neutrality goals are finally suggested. 
## 
## Topic 1: Emissions
## V9: 10.1016/j.jclepro.2022.134933
## Considering the environmental governance dilemma caused by environmental decentralization, this study aims to explore whether the strategic synergy between local and neighborhood environmental regulations can be an essential tool to improve green innovation efficiency and achieve sustainable development. Using the data of industrial firms from 2005 to 2019, and employing network slack-based measure and Tobit regression, this study provides empirical evidence that (1) the green innovation efficiency shows an upward trend in fluctuations but still has great room for improvement; (2) the direct impact of local environmental regulation on green innovation is positive, but the indirect impact through forcing firms to transfer into the neighborhood with loose regulation is negative, that is, the industrial transfer plays a suppression effect; (3) the strategic synergy of environmental regulations has U-shaped and direct effect on green innovation and also has a positive indirect effect through inhibiting the firm&#39;s behavior transferring into the neighborhood. This study reveals the influence mechanism of the strategic synergy of local-neighborhood environmental regulations and offers empirical evidence to explain the reason why synergistic environmental governance can effectively promote green innovation, which provides the theoretical guidance for government to formulate environmental policies and construct an environmental governance system. 
## 
## Topic 1: Emissions
## V10: 10.1016/j.apenergy.2022.119012
## Carbon emissions trading is an important measure to promote high-quality economic development. Based on the panel data of 30 provincial administrative regions in China from 2008 to 2017, this paper uses the difference-in-differences method to analyze the impact of carbon emissions trading on green technology innovation. The results show that: (1) Carbon emissions trading inhibits green technology innovation in the current stage, but greatly reduces carbon emissions and carbon intensity; (2) Carbon price and R&amp;D investment are the mainly working channels, carbon emissions trading has a crowding-out effect on corporates’ R&amp;D investment and increases the carbon trading price, which in turn inhibits green technology innovation; (3) Carbon emissions trading has a stronger inhibitory effect on green technology innovation in eastern regions and regions with low emission intensity. Local government competition positively moderates the green technology innovation effect of carbon emissions trading. However, the command-controlled environmental regulations and the scale of the carbon emission quota have opposite effects, with the compatibility of environmental regulation tools remaining to be resolved. Therefore, it is necessary to control the scale of carbon emission quota to diversify the subjects of carbon trading and strengthen the impact of market-incentive environmental regulation tools. 
## 
## Topic 1: Emissions
## V11: 10.1002/sd.2440
## The rapid development of China&#39;s economy has also led to serious environmental problems, which have seriously restricted China&#39;s sustainable development. To this end, the Chinese government has continued to explore environmental governance. So, can environmental governance policies promote industrial green transformation, so as to achieve the goal of sustainable development? Will environmental policy competition among local governments have an impact on them? These issues are the focus of this article. Based on the provincial panel data of China from 2006 to 2021, this article empirically analyzes the relationship between environmental governance, local government competition, and industrial green transformation. The results found that there was a “U” type nonlinear relationship between environmental governance and industrial green transformation. On the whole, most regions of China are still in the development stage where environmental governance is not conducive to industrial green transformation. There is competition for environmental governance policies among local governments in China, and the characteristics of “race to the bottom” are more significant. In the East developed areas, there is a more significant “yardstick competition” game state. In the Midwest undeveloped areas, it is manifested as competition for resources by deregulation of the environment, thereby promoting short-term economic growth. In addition, local protection behaviors negatively regulate the impact of environmental governance on industrial green transformation, that is, inhibiting local industrial green transformation by causing labor market mismatch. This study can provide valuable insights for policymakers aiming to adopt measures to achieve the sustainable development of the economy and environment. 
## 
## Topic 1: Emissions
## V12: 10.1016/j.enpol.2021.112252
## This paper aims to provide new evidence on the relationship between green investment and firm performance through micro-level data. Data of energy listed firms in China from 2008 to 2017 are used here to explore this relationship. The research results show that green investment has a significant and positive correlation with financial performance, that is, increasing green investment helps improve financial performance. In the third year after investment in energy conservation and emission reduction, financial performance has been significantly improved. Additionally, environmental tax, government subsidies, and technological innovation have different positive moderating effects on green investment in promoting financial performance, and this result is more obvious in the long-term performance. Lastly, this paper finds that green investment helps reduce environmental violations and promote environmental performance, and environmental performance can strengthen the impact of green investment in improving the long-term performance of firms. This conclusion implies that firms should take environmental investment as its long-term strategy. 
## 
## Topic 1: Emissions
## V13: 10.1016/j.eneco.2023.106929
## As a market incentive-based environmental regulation, the carbon emission trading (CET) policy is an important tool for China to achieve “carbon peak” and “carbon neutrality” goals. However, it is not clear how the CET policy will affect companies&#39; overcapacity. Given this, based on collecting the list of the CET pilot companies, this paper uses the difference-in-differences (DID) model to explore the impact of the CET policy on companies&#39; overcapacity. It&#39;s found that the CET policy will significantly aggravate companies&#39; overcapacity. The results of the mechanism analysis show that the CET policy will expand companies&#39; fixed assets investment, reduce their turnover of fixed assets, and increase the credit support and green subsidies provided by the government to companies, thereby leading to a disorderly expansion of capacity. Besides, heterogeneity analysis shows that companies&#39; overcapacity aggravation caused by the CET policy is more prominent in regions with low marketization levels, backward economic development levels, and weak environmental regulation intensity, state-owned companies and companies with low R&amp;D levels. This paper not only provides new research literature for the CET policy and overcapacity, but also provides more empirical evidence at the micro level for the continuous improvement of the national CET market. 
## 
## Topic 1: Emissions
## V14: 10.1016/j.jclepro.2023.138112
## Environmental regulation, as a safe line of defense for the ecological environment, is an important practical exploration to guide green innovation and to promote high-quality development of enterprise. This research examines the impact of environmental regulation on high-quality development of enterprise and the realization path based on the data of Chinese A-share listed enterprises from 2010 to 2020. The fixed-effect model reveals a significant positive correlation between environmental regulation and high-quality development of enterprise. Moreover, the chain intermediation model is used to examine the realization path. Specifically, environmental regulation hinders enterprises&#39; green management innovation and thus has a negative impact on high-quality development of enterprise, which is manifested as a masking effect. Environmental regulation has a positive impact on the high-quality development of enterprise through enhancing green technology innovation or the intermediation chain of &quot;promoting green technology innovation - driving green management innovation&quot;. Heterogeneity analysis reveals that environmental regulation has a facilitating effect on the high-quality development of enterprises for non-state-owned enterprises, and enterprises within the carbon emission trading pilot areas. In addition, environmental regulation has the strongest promoting effect on high-quality development of enterprise in the mature stage, followed by the growth stage. The research provides actionable paths for the government to optimize the institutional system of ecological environment construction and accelerate the modernization process of environmental governance. 
## 
## Topic 1: Emissions
## V15: 10.1016/j.jclepro.2022.130785
## Nowadays, countries around the world are increasingly emphasizing the green concept in their foreign investment cooperation. As an important means to guide the green development of China&#39;s economy, green credit policy may affect the overseas investment decisions of Chinese enterprises and ultimately affect the investment efficiency. Based on the overseas direct investment data of Chinese A-share listed companies from 2007 to 2019, this paper calculates the overseas investment efficiency at the enterprise level by expanded Richardson model, and further tests the influence of green credit policy on the overseas investment efficiency of Chinese enterprises. The results show that green credit policy can improve the efficiency of enterprises&#39; overseas investment, especially for state-owned enterprises and enterprises in low environmental regulations areas. Therefore, China&#39;s financial institutions should strengthen the implementation of green credit policies and formulate differentiated credit schemes for enterprises with different characteristics under the supervision of relevant departments. The results of this paper verify the effectiveness of the green credit policy, which is very important for accelerating the green development of China&#39;s overseas investment. 
## 
## Topic 1: Emissions
## V16: 10.1016/j.jclepro.2023.136830
## As China&#39;s longest-implemented market-based environmental regulation policy, emission charges have contributed outstandingly to improving the ecological environment and reducing firms&#39; pollution emissions. We assess the impact of emission charges on the innovation quality of Chinese firms based on the perspective of firms&#39; breakthrough technological innovation. By observing Chinese A-share listed firms with heavy pollution from 2011 to 2020, we find that corporate breakthrough technological innovation positively moderates the emission charges to enhance the quality of corporate innovation. Our mechanism tests suggest that emission charges and breakthrough technological innovation positively affect innovation quality by improving the means of green innovation of enterprises. In addition, heterogeneity analysis shows that emission charges and firms&#39; breakthrough technological innovation are only effective in improving the innovation quality of state-owned firms, and the effect on the innovation quality of non-state-owned firms is not significant. Our findings suggest that the imposition of emission charges and incentives for firms to embark on breakthrough technological innovation can effectively improve the quality of innovation. 
## 
## Topic 1: Emissions
## V17: 10.1016/j.jclepro.2021.126326
## In China, green credit aims to guide the flow of credit funds by regulating the quantity and price of credit, so as to support the development of green industries and curb the emission behavior of polluting enterprises. Based on the behavior of microeconomic entities we construct a Dynamic Stochastic Genernal Equilibrium (DSGE) model to analyze the output and welfare effect of green credit in China. Specifically, the incentive green credit is taken as an example to carry out an empirical study, and we further quantitatively measure the output and welfare effect of green credit under different environmental regulations. We find that both price-based and quantity-based green credit have obvious output, environment, health and utility welfare effect, which is conducive to the green upgrading of industrial structure and can achieve a win-win situation of output and environment in China. However, there are some differences in transmission mechanism and utility welfare. In addition, in the short term, the environmental tax regulation inhibits the economic expansion effect of green credit, and in the long term, this inhibitory effect gradually disappears. The DSGE comprehensive evaluation framework constructed in this paper expands the application scope of DSGE model, and provides a quantitative theoretical model for the mechanism analysis of China&#39;s green credit policy and similar policies. Furthermore this study provides theoretical and quantitative basis for the formulation of green credit policies and the improvement of macro-control policies in China. 
## 
## Topic 1: Emissions
## V18: 10.1016/j.eneco.2021.105099
## Using the quasi-experimental method, this research investigates the impact of green credit policy on the upgrade of energy-intensive enterprises from the perspective of credit allocation efficiency. Through the panel data of listed companies in China, this study finds that the green credit policy under the Green Credit Guidelines in 2012 (GCG2012) has a significantly negative effect on the research and development (R&amp;D) intensity and the total factor productivity (TFP) of treated firms. Empirical evidence also shows that the GCG2012 significantly reduces bank credit but increases trade credit. Consequently, the substitution hypothesis is established. Furthermore, GCG2012 has reduced the allocation efficiency of bank credit within energy-intensive industries. As an improved green credit policy to encourage enterprises to invest in energy efficiency, the Energy Efficiency Credit Guidelines in 2015 (EECG2015) increases both the bank credit and the fixed asset investment, whereas no increase in R&amp;D intensity or TFP is found. These findings are enlightening for designing better green credit policies. 
## 
## Topic 1: Emissions
## V19: 10.1016/j.jclepro.2023.137078
## This paper deconstructs industrial green transformation into two aspects, industrial efficiency green transformation and industrial structure green transformation. We examine the impact of digital finance development on industrial green transformation using data from Chinese cities and listed companies. The results showed that: (1) digital finance can contribute to the green transformation of industrial structure distinctly but has an inhibiting effect on the green transformation of industrial efficiency. (2) Digital finance can promote capital-using technological progress and support industrial green transformation with its price effect and scale effect. (3) Higher government environmental preferences and governance capacity can strengthen the green transformation effect of digital finance. Strengthening financial supervision and narrowing the digital divide will help digital finance better support industrial green transformation. Digital finance development can more effectively promote the industrial green transformation of non-SOEs and enterprises with high financing constraints and high financial dependence. (4) Considering the financial attributes and highly polluting characteristics of real estate, digital finance exacerbates the pollution of the real estate industry. This study helps clarify the mechanism of digital finance supporting green development and provides some ideas for China&#39;s digital finance development and green economy transformation. 
## 
## Topic 1: Emissions
## V20: 10.1016/j.enpol.2023.113523
## The relationship between green finance and energy efficiency is of great significance for China to solve the environmental pollution problems caused by excessive energy consumption and high carbon emissions. In this paper, the method of non-radial distance function (NDDF-DEA) is adopted to estimate the energy efficiency for 30 provinces in China. In addition, the spatial Durbin model is established based on provincial panel data from 2005 to 2019 in China to investigate the mechanisms for the impact of green finance on energy efficiency. Results show that with the characteristic of spatial dependence, the energy efficiency in China decreases with time. The green finance has spatial spillover effects, and can improve energy efficiencies in local and neighboring areas. In addition, Internet development and environmental regulation are important channels for green finance to further promote energy efficiency. Conclusions also indicate that the spatial spillover effect is positive in the eastern and western regions and negative in the central regions, proving the regional heterogeneity of spatial spillover effect for green finance. The empirical evidence and policy recommendations are provided in this paper to more rationally enhance the promotion impact of green finance on energy efficiency. 
## 
## Topic 1: Emissions
## V21: 10.1016/j.spc.2021.03.016
## Improving energy and carbon emission performance is of practical significance to the green economy, and Internet development may help achieve this goal. However, the effect of the Internet on energy and carbon emission performance has not been systematically explained. Based on Chinese provincial data from 2006 to 2017, this paper uses the non-radial directional distance function (NDDF) to measure energy and carbon emission performance and then constructs a two-way fixed-effect model. The results indicate that: (1) Internet development significantly improves energy and carbon emission performance, but there is significant spatiotemporal heterogeneity. (2) The analysis of the influence mechanism shows that Internet development is mainly to improve energy and carbon emission performance by promoting industrial structure upgrading and technology diffusion. (3) local government intervention weakens the effect of the Internet, while market-oriented development is conducive to the positive role of Internet. This study helps policy-makers better understand the impact of the Internet on energy and the environment, and formulate effective policies to achieve energy conservation and emission reduction. 
## 
## Topic 1: Emissions
## V22: 10.1016/j.jclepro.2021.126607
## This study breaks through the limitation of traditional analysis that only considers the economic development factors and further explains the reasons for the existence of inter-provincial market segmentation in China from the perspective of industrial transformation performance by combining environmental protection. The study examines panel data from 30 provinces in China from 2004 to 2017. It uses a super-efficiency slacks-based measure model to evaluate the performance of industrial transformation considering environmental protection and a fixed-effect model to analyze the impact of market segmentation on industrial transformation performance. The results show that market segmentation has an inverted “U” effect on the region&#39;s immediate and future industrial transformation performance. That is, for most regions, market segmentation can significantly improve the performance of industrial transformation. For regions with higher environmental regulations, market segmentation is more conducive to the improvement of industrial transformation performance, which shows that local governments will give up some market efficiency while carrying out environmental protection. The research results not only confirm the relationship between market segmentation and industrial transformation performance but also have important promotional significance for China&#39;s economic green development. 
## 
## Topic 1: Emissions
## V23: 10.1016/j.enpol.2023.113595
## Carbon emissions trading schemes (ETS) are critical environmental regulation policies that can reduce regional CO2 emissions and promote innovation. China&#39;s carbon ETS, applied at the city level, has yet to be studied for its effects on technology transfer. We investigate how carbon ETS affects technology transfer in China&#39;s prefecture-level cities using the difference in differences (DID) method. The results show that compared to the control group, technology transfer strength in the treatment group increased by 0.185 units on average. There are two directions in which technology transfer strength can be significantly increased: in-strength and out-strength. In addition, we find that carbon ETS can greatly promote technology transfer cross provinces. Furthermore, we explored the heterogeneous effects of city scale and innovation environment on technology transfer. The carbon ETS also promotes more significant effects on technology transfer in big cities. A city with a low innovation environment is not conducive to promoting technology transfer in comparison with a city with a good innovation environment. 
## 
## Topic 1: Emissions
## V24: 10.1016/j.rser.2021.111779
## Under the dual pressures of carbon emissions reduction targets and high-quality economic development, environmental rights trading was widely used to promote win-win situation between economy and environment. Taking China&#39;s carbon emissions trading policy (CETP) as the research object, and based on 2008 to 2017 panel data from Chinese provinces, this study employed a spatial difference-in-differences (SDID) model based on an endogenous spatio-temporal weight matrix to explore the direct incentive and spatial spillover effects of CETP on green innovation (GI). It was found that the CETP promoted GI in the pilot areas, and the policy spillover effect stimulated GI in neighboring provinces through innovative growth poles, which was conducive to the overall reduction of carbon emissions. The study also found that the GI effectiveness was heterogeneous due to the differences in the innovation growth pole life cycle. The Beijing Tianjin Innovative Growth Pole (BTIP) was found to have a GI polarizing effect on the neighboring regions, the Shanghai Innovative Growth Pole (SIP) and the Hubei Chongqing Innovative Growth Pole (HCIP) had a GI diffusion effect on the neighboring regions, and the Guangdong Innovative Growth Pole (GIP) developed towards the interactive effect stage, at which time the CETP no longer played a significant role in promoting provincial or neighboring provincial GI. Finally, the action test mechanism found that the government competition (GC) and its interaction with the market competition (MC) both enhanced the polarization effect of the innovation growth poles, and therefore seriously hindered the CETP spillover effect on GI. Based on these findings, rich experience is provided for CETP promotion and application, which is expected to provide the policy basis for achieving carbon peak and carbon neutrality targets. 
## 
## Topic 1: Emissions
## V25: 10.1016/j.jclepro.2021.125868
## The pilot program of low-carbon cities is of great significance for China&#39;s overall economic and social development. The construction of low-carbon cities contributes to adjusting the industrial structure, optimizing the energy structure and accelerating institutional reforms. Using the panel data of 285 prefecture-level cities during 2006–2015, this paper takes the pilot program of low-carbon cities as a quasi-natural experiment to detect the impact of the pilot program on industrial structure upgrading and its possible mechanism. The results reveal that the construction of low-carbon cities displays a positive impact on industrial structure supererogation while it has little effect on industrial structure rationalization. Ventilation coefficient is regarded as an instrumental variable to address endogenous problems. The conclusions are still tenable after a series of robustness tests. Mechanism analysis shows that the pilot program can bolster industrial structure supererogation via technological innovation and the reduction of the proportion of high-carbon industries. The regional heterogeneity analysis indicates that the influence of the pilot program on the industrial structure supererogation in the central and western areas is more positive than that in the eastern area, whereas the impact of the pilot program on the industrial structure rationalization is not significant in the three areas. The heterogeneity analysis of environmental law enforcement indicates that the pilot program can more obviously bump up the industrial structure supererogation in cities with strong law enforcement intensity. The impact on the industrial structure rationalization is not evident in cities with weak law enforcement intensity, but the industrial structure rationalization is significantly improved in cities with strong law enforcement intensity. This paper provides insights for realizing the integrated development of low-carbon cities and industrial structure upgrading. 
## 
## Topic 2: Economy
## V1: 10.1016/j.jclepro.2019.04.281
## In this study, the causal link amid economic growth, fossil fuel energy consumption, carbon emissions and oil price was empirically tested from 1990 to 2015 by using a panel of 22 African countries. The sample of African countries was divided into two subgroups namely; Oil exporters and Non-oil exporters. Using the PMG panel ARDL and panel econometric methods, heterogeneity and cross-sectional dependence were considered to examine the long and short-term dynamic relationships as well as validity of a proposed model. Findings from the Pesaran-Yamagata homogeneity test, Pesaran CD test, CIPS and CADF panel unit root tests and Westerlund-Edgerton bootstrap panel cointegration indicated, the panel time series data has heterogeneity and cross-sectional dependence, analysed variables are stationary and cointegrated respectively. With respect to the PMG panel ARDL estimation method it was evidenced that there exist, (i) a bilateral causal link flanked by fossil fuel energy consumption and economic growth as well as fossil fuel energy consumption and carbon emissions in long and shot-terms for all panels, (ii) a unilateral causality from carbon emissions to economic growth in long-term and short-term for non-oil exporters nonetheless a bilateral causal relationship only in long-term for oil exporting countries, and (iii) a unilateral cause-and-effect link from oil prices to economic growth, energy consumption (fossil fuel) and carbon emission across all country groups in long and short-terms. Policy recommendations are further discussed. 
## 
## Topic 2: Economy
## V2: 10.1016/j.energy.2020.119684
## This paper examines the effect of remittance income on energy consumption and the economy between 1976 and 2019 in the four highest remittance recipient nations of South Asia. The detailed analysis explores long-run and directional relationships utilizing stationary tests, the panel cointegration test, dynamic ordinary least square (DOLS), fully modified ordinary least square (FMOLS), and Granger causality tests using a Vector Error Correction Model (VECM). The study reveals the presence of long-run relationships among remittances, energy consumption, GDP and urbanization. Cointegrating regression identifies that remittance income, economic growth and urbanization individually have a positive and significant effect on energy consumption in the long-run. Results showed that a 1% increase in remittance income results in a 0.045% increase in energy consumption. Among nations analyzed, this effect was found to be strongly dominant in case of Bangladesh and Pakistan. The statistically significant and negative coefficient of the error correction term confirms long-run causality from remittance, GDP, and urbanization toward energy consumption. Variance decomposition analysis demonstrated that energy usage is heavily impacted by remittance inflows among studied variables. Policy implications of this study suggest that higher remittance inflows will increase energy consumption in South Asian countries, engendering positive economic growth and sustainable development. 
## 
## Topic 2: Economy
## V3: 10.1016/j.egyr.2023.06.020
## Adverse consequences are observed in developing countries due to the impact of the globalization process. Therefore, our study aims to empirically verify whether globalization escalates carbon dioxide emissions in selected N-11 (next-11) countries between 1990 and 2019. The study also analyzes how per capita GDP, per capita GDP2, population growth, and renewable energy consumption affect carbon emissions. For this reason, the researchers used several econometric methods, including the slope homogeneity test, the cross-sectional dependency test, the panel unit root test, the panel cointegration test, the method of moment&#39;s panel quantile regression analysis, and the Wald test. The estimated results of panel quantile regression show how carbon emissions change across a range of quantiles (0.1 to 0.9). The findings show that per capita GDP significantly impacts the overabundance of carbon emissions in N-11 countries. Over time, the study found that the positive coefficient value of per capita GDP decreased from the first to the last (7.41 to 5.87), leading to the validation of the EKC hypothesis. The adverse correlation between per capita GDP2 and environmental contamination confirms that the Environmental Kuznets Curve hypothesis is valid for selected N-11 countries. Globalization deteriorates the environment by directly affecting CO2 emissions. It increases monotonically from the lower quantile to the upper quantile (0.972 to 1.002). At the quantile level of 0.1 to 0.9, population growth and renewable energy consumption increase impede carbon dioxide emissions in these selected countries. Coefficient values in the quantile 0.1 to 0.9 (-0.35 to -0.53) suggest that governments can reduce carbon emissions more due to renewable energy consumption over time. But the negative coefficient values of the population (-0.97, -0.93, -0.90, -0.88, -0.86, -0.85, -0.83, -0.81, and -0.77) decrease from the lower quantile to the upper quantile. The Wald test supports the asymmetric effects of different quantiles. As a robustness check of estimators, the study used FMOLS, DOLS, and CCR, which show the variables’ long-run elasticity. The research developed targeted policy recommendations for sustainably mitigating carbon emissions based on the above results. 
## 
## Topic 2: Economy
## V4: 10.1016/j.enpol.2014.02.029
## In this article, we explore the long-run cointegration between output, capital and energy consumption, in per worker terms, for Albania, Bulgaria, Hungary and Romania. We use the augmented Solow (1956) model and the ARDL bounds procedure ( Pesaran et al., 2001) to examine the short-run and long-run effects of energy and capital on output (in per worker terms). We also conduct causality test using the Toda and Yamamoto (1995) non-causality procedure. Our results show the existence of long-run cointegration between output per worker and energy per worker for all the four countries. We find that energy per worker have a dynamic short-run positive effect in Albania (0.37%), Bulgaria (0.25%), Hungary (0.36%) and Romania (0.68%), and a long-run positive effect in Bulgaria (0.32%) and Romania (0.63%) which duly indicate that energy consumption has a momentous long-run effect in these two countries. The causality results indicate a unidirectional causation from output per worker to energy per worker for all the four countries, and from capital per worker to energy per worker for Albania and Romania. Consequently, a balance between effective energy consumption and sound energy conservation policies are likely to support economic growth in the four countries © 2014 Elsevier Ltd. 
## 
## Topic 2: Economy
## V5: 10.1016/j.esr.2023.101182
## Exploring the factors that affect energy intensity is a worthy research topic because a decline of energy intensity lowers greenhouse gas emissions and increases energy security. Therefore, using the World Bank data from 2000 to 2020, this study examined how industrialisation, trade openness, financial development, and urbanisation affected energy intensity in 12 Newly Industrialized Countries (NICs). We used panel autoregressive distributed lag/pooled mean group (ARDL/PMG), cross-sectional ARDL (CS-ARDL), and fully modified ordinary least squares (FMOLS) to determine the long- and short-run effects of explanatory variables on the dependent variable, energy intensity. All variables are found stationary after the unit root test and cointegration test confirms that variables are linked in the long run. Panel ARDL/PMG and CS-ARDL results show that industrialisation increases energy intensity in the long and short runs. These tests also show that financial development increases energy intensity over time but not immediately. Trade openness decreases energy intensity in the long run but not in the short run. Urbanisation has negligible effects. Dumitrescu and Hurlin (2012) Granger causality test result shows that energy intensity causes trade openness. The test also shows two-way causality between industrialisation, financial development, and energy intensity. Results-driven policy recommendations, such as investment in efficient and environmentally friendly technologies, and facilitation of trade openness, are made following the conclusion. 
## 
## Topic 2: Economy
## V6: 10.1016/j.eneco.2018.07.022
## This paper applies panel vector autoregression (PVAR) along with a system-generalized method of moment (System-GMM) to examine the dynamic causal relationship between economic growth, carbon emissions and energy consumption for 116 countries over the period 1990–2014. Using multivariate model, the empirical results from this study have established key relationships which have important policy implications. First, at the global and regional levels, economic growth does not cause energy consumption. Second, with the exception of the global and Caribbean-Latin America, economic growth has no causal impact on carbon emissions, however, economic growth has a negative impact on carbon emissions at the global level and the Caribbean-Latin America. Third, carbon emissions positively cause economic growth. Fourth, energy consumption positively causes economic growth in sub-Saharan Africa while it negatively causes economic growth at the global, Middle East and North Africa (MENA), Asia-Pacific and Caribbean-Latin America. Fifth, energy consumption positively causes carbon emissions in MENA but causes carbon emissions negatively in sub-Saharan Africa and Caribbean-Latin America. Lastly, with the exception of MENA and the global sample, carbon emissions do not cause energy consumption. The impulse response function shows evidence of Environmental Kuznets curve at the global scale and sub-Saharan Africa. The policy implications of this paper are discussed. 
## 
## Topic 2: Economy
## V7: 10.1016/j.renene.2022.03.043
## This paper investigates the relationship between renewable energy consumption and ecological footprint in Saudi Arabia by considering the important role of economic growth in the environmental function during 1980–2017 periods. Capital and trade openness are determinants of environmental degradation. To determine the cointegration between the variables, we relied on the bootstrap autoregressive distributed lag (ARDL) bound test. We also used the Granger causality based on the bootstrap ARDL approach to identify causal relationships between the factors of environmental degradation in the presence of structural break. The empirical results show the presence of cointegration between variables with a structural break. Furthermore, the results of the ARDL model state that an increase in human capital and renewable energy consumption improves environmental quality (decreasing the ecological footprint), while an increase in trade openness and GDP deteriorates environmental quality (increasing the ecological footprint). In the short run, the results of the VECM model show the existence of a bidirectional causal relationship between human capital and trade openness. GDP causes the ecological footprint, while human capital causes renewable energy consumption. Moreover, there is a causal relationship from GDP and renewable energy consumption to trade openness. In the long run, all variables cause renewable energy consumption on the one hand, and trade openness on the other. In view of these results, policy makers have an interest in implementing favorable measures that reduce the ecological footprint and improve the quality of the environment by using renewable energy consumption as an economic tool. 
## 
## Topic 2: Economy
## V8: 10.1016/j.enpol.2010.08.045
## This paper examines dynamic causal relationships between pollutant emissions, energy consumption and output for a panel of BRIC countries over the period 1971-2005, except for Russia (1990-2005). In long-run equilibrium energy consumption has a positive and statistically significant impact on emissions, while real output exhibits the inverted U-shape pattern associated with the Environmental Kuznets Curve (EKC) hypothesis with the threshold income of 5.393 (in logarithms). In the short term, changes in emissions are driven mostly by the error correction term and short term energy consumption shocks, as opposed to short term output shocks for each country. Short-term deviations from the long term equilibrium take from 0.770 years (Russia) to 5.848 years (Brazil) to correct. The panel causality results indicate there are energy consumption-emissions bidirectional strong causality and energy consumption-output bidirectional long-run causality, along with unidirectional both strong and short-run causalities from emissions and energy consumption, respectively, to output. Overall, in order to reduce emissions and not to adversely affect economic growth, increasing both energy supply investment and energy efficiency, and stepping up energy conservation policies to reduce unnecessary wastage of energy can be initiated for energy-dependent BRIC countries. © 2010 Elsevier Ltd. 
## 
## Topic 2: Economy
## V9: 10.1016/j.renene.2020.03.135
## Using a comparative approach, this study examines the nexus among renewable energy consumption, economic growth, trade, urbanisation and CO2 emissions for Australia and Canada for the period 1960–2015. The Autoregressive Distributed Lag (ARDL) bounds tests are used to explore the long-run relationships amongst the selected variables and the causal relationships are examined by the vector error correction model (VECM) Granger causality tests. The findings suggest that there is evidence of the long run relationships amongst the variables. The results in Australia indicate that in both the long run and short run, economic growth increases CO2 emissions, whereas in the short run, the trade and renewable energy consumptions decrease CO2 emissions. The VECM causality tests for Australia indicate that in the short-run, the economic growth, trade, and renewable energy consumption Granger cause CO2 emissions; whereas the long-run causal relationships are found among CO2 emissions, economic growth, trade and renewable energy consumption. In the case of Canada, the results show that in both the long run and short run, trade increases CO2 emissions, while in the long run the economic growth and urban population increase CO2 emissions. The VECM causality analyses show the long-run bidirectional causality among CO2 emissions, economic growth, and renewable energy consumption. In addition, the CO2 emissions tended to decrease in Australia but not in Canada after each country officially ratified the Kyoto Protocol in 2007 and 2002, respectively. Policy recommendations are made based on these findings. 
## 
## Topic 2: Economy
## V10: 10.1016/j.enpol.2013.11.040
## This paper examines the impact of natural gas consumption, real gross fixed capital formation and trade on the real GDP in the case of Tunisia over the period 1980-2010. We use an Autoregressive Distributed Lag (ARDL) bounds testing approach to test for cointegration between the variables. The Toda-Yamamoto approach is then used to test for causality. Our findings indicate the existence of a long-term relationship between the variables. Natural gas consumption, real gross fixed capital formation and trade add in economic growth. Natural gas consumption, real gross fixed capital formation and real trade cause real GDP in Tunisia. These findings open up new insights for policymakers to formulate a comprehensive energy policy to sustain economic growth in the long-term. •We study how gas consumption, fixed capital formation and trade affect GDP in Tunisia.•We use auto-regressive distributed lag bounds testing approach and causality tests.•Gas consumption, real gross fixed capital formation and trade add in economic growth. © 2013 Elsevier Ltd. 
## 
## Topic 2: Economy
## V11: 10.1016/j.esr.2022.100986
## The empirical literature examines the effect of exchange rate on energy demand. However, the existing literature fails to investigate the effect during different states of energy demand, that is, when energy demand is relatively high and when it is quite low. We extend the prior literature by examining the effect of exchange rates on various states of energy demand in G7 countries. We use the Quantile ARDL (QARDL) model and compare its results with standard ARDL and nonlinear ARDL models. In addition, we use the Granger causality in Quantile (GCQ) test for robustness purposes. The standard ARDL and nonlinear ARDL estimates show that cointegration exists in the context of France only. In contrast, QARDL estimates support cointegration in all sample countries. Moreover, the QARDL estimates indicate that the short-run and long-run effect varies across various quantiles of the energy demand, suggesting different policy implications during different states of the energy demand. Finally, GCQ estimates suggest that the exchange rate causes energy demand across various quantiles of the exchange rate and energy demand. In contrast, energy demand does not cause exchange rate at most quantiles. These findings indicate that devising policies without considering the different states of energy demand may lead to unfavourable consequences. 
## 
## Topic 2: Economy
## V12: 10.1016/j.eneco.2017.01.023
## This paper investigates the asymmetric relationship between energy consumption and economic growth by incorporating financial development, capital and labour into a production function covering the Indian economy from 1960Q1–2015Q4. The nonlinear autoregressive distributed lag bounds testing approach is applied to examine the asymmetric cointegration between the variables. An asymmetric causality test is also employed to examine the causal association between the considered variables. The results indicate cointegration between the variables in the presence of asymmetries. The asymmetric causality results show that only negative shocks to energy consumption have impacts on economic growth. In the same vein, only negative shocks to financial development have impacts on economic growth. By contrast, symmetrically, capital formation causes economic growth. Finally, over the study period, a neutral effect exists between the labour force and economic growth in India. The implications of these results for growth policies in India are also discussed. 
## 
## Topic 2: Economy
## V13: 10.1016/j.energy.2011.03.031
## This study attempts to examine empirically dynamic causal relationships between aggregate output, energy consumption, exports, capital and labour in the case of Turkey using the time series data for the period 1968-2008. This research tests the interrelationships between the variables using the bounds testing to cointegration procedure. The bounds test results indicate that there exists a long-run relationship between the variables in which the dependent variable is aggregate output. Within this study, three competing sets of hypotheses regarding the relationship between aggregate output, exports and energy consumption are tested. An augmented form of Granger causality analysis is conducted amongst the variables. In the long-run, causality runs interactively through the error correction term from labour, capital, exports and energy consumption to aggregate output. In the short-run, two important bilateral causalities were identified: between energy consumption and aggregate output, between exports and aggregate output. The short-run causality testing reveals further the existence of a unilateral causality running from exports to energy consumption too. The long-run relationship of aggregate output, energy consumption, exports, capital and labour equation is also checked for the parameter stability. The results also provide some important policy recommendations. © 2011 Elsevier Ltd. 
## 
## Topic 2: Economy
## V14: 10.1016/j.renene.2021.02.072
## Renewable energy attracts attentions by removing the harmful effects of fossil fuels and protecting the environment. The focus of this study is to investigate GDP per capita, carbon dioxide emissions per capita, technological innovation and trade as the determinants of renewable energy production for the selected Latin American countries during the 1991–2014 period. Technological innovation factor is rarely investigated as one of the determinants of renewable energy. The country group includes Argentina, Brazil, Mexico, Colombia, Chile and Guatemala. Empirical results are obtained by applying panel estimation methods. Cointegration relationship among the analyzed variables is confirmed by Pedroni and Westerlund panel cointegration tests. According to the empirical analysis, GDP per capita, technological innovation and trade have positive and statistically significant impact on renewable energy production per capita. On the other hand, it is found that carbon dioxide emissions and renewable energy production are negatively associated. In addition, the elasticity estimates with respect to technological innovation and trade are found as close to each other. The policy implications of the empirical findings are argued as well. 
## 
## Topic 2: Economy
## V15: 10.1016/j.jclepro.2019.04.210
## This study determines the dynamic linkages between globalization, financial development and carbon emissions in Asia Pacific Economic Cooperation (APEC) countries in the presence of energy intensity and economic growth under the framework of Environment Kuznets Curve (EKC). This study employs the panel data from 1990 to 2016, Westerlund cointegration technique to find long-run cointegration, and Continuously Updated Bias-Corrected (CUP-BC) and Continuously Updated Fully Modified (CUP-FM) methods to check the long-run elasticities between the variables. Empirical results indicate that globalization and financial development significantly reduce carbon emissions, but economic growth and energy intensity increase them. These results support the EKC hypothesis for APEC countries. The Dumitrescu and Hurlin causality analysis shows that globalization Granger causes CO2 emissions. Globalization also causes financial development and energy intensity. A feedback effect exists between financial development and CO2 emissions. Furthermore, financial development causes economic growth but similar is not true from opposite-side in Granger sense. Finally, this study presents important policy implications with respect to APEC countries. 
## 
## Topic 2: Economy
## V16: 10.1016/j.renene.2019.06.054
## This study investigates the short-term and long-term impacts of economic growth, trade openness and technological progress on renewable energy use in Organization for Economic Co-operation and Development (OECD) countries. Based on a panel data set of 25 OECD countries for 43 years, we used the autoregressive distributed lag (ARDL) approach and the related intermediate estimators, including pooled mean group (PMG), mean group (MG) and dynamic fixed effect (DFE) to achieve the objective. The estimated ARDL model has also been checked for robustness using the two substitute single equation estimators, these being the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS). Empirical results reveal that economic growth, trade openness and technological progress significantly influence renewable energy use over the long-term in OECD countries. While the long-term nature of dynamics of the variables is found to be similar across 25 OECD countries, their short-term dynamics are found to be mixed in nature. This is attributed to varying levels of trade openness and technological progress in OECD countries. Since this is a pioneer study that investigates the issue, the findings are completely new and they make a significant contribution to renewable energy literature as well as relevant policy development. 
## 
## Topic 2: Economy
## V17: 10.1016/j.eneco.2011.12.008
## This study uses panel cointegration regression techniques to examine the relationship between energy consumption, output and trade in a sample of 7 South American countries covering the period 1980 to 2007. Panel cointegration tests show a long-run relationship between 1) output, capital, labor, energy, and exports and 2) output, capital, labor, energy, and imports. Short-run dynamics show a bi-directional feedback relationship between energy consumption and exports, output and exports and output and imports. There is evidence of a one way short-run relationship from energy consumption to imports. In the long-run there is evidence of a causal relationship between trade (exports or imports) and energy consumption. These results have implications for energy policy and environmental policy. One important implication of these results is that environmental policies designed to reduce energy use will reduce trade. This puts environmental policy aimed at reducing energy consumption at odds with trade policy. © 2011 Elsevier B.V. 
## 
## Topic 2: Economy
## V18: 10.1016/j.eneco.2013.11.002
## We examine the long-run relation and short-run dynamics between energy consumption and output in a panel of 14 oil-exporting countries over 1980-2007. Panel unit root tests, which account for common cross-sectional factors, fail to reject non-stationarity in both variables. Thus, we explore their long-run relation and short-run dynamics using three alternative panel estimation techniques - dynamic fixed effect, pooled and mean-group estimators before and after accounting for common cross-sectional factors. These estimators allow for various degrees of heterogeneity in long-run parameters and short-run dynamics. The results based on the mean group estimator with common correlated effects suggest (a) a stable relation between energy consumption and output; (b) bi-directional causality in both long- and short-run; and (c) the robustness of the long-run causality results to the inclusion of additional variables. As such, environmental policies designed to curtail energy may have significant long-run ramifications for economic growth, and policies designed to promote economic growth may have adverse environmental consequences. © 2013 Elsevier B.V. 
## 
## Topic 2: Economy
## V19: 10.1016/j.jclepro.2017.05.129
## The paper explores the nexus between greenhouse gas, financial development, energy consumption, trade and urbanization in 34 upper middle income countries from Asia, Europe, Africa and America (South and North) by using panel data from 2001 to 2014. Vector error correction model explored different results for different regions such as the causality of greenhouse gas for Europe and America; financial development for Asia; trade openness for Asia, Europe and America; and urbanization for Africa and America. The causality of greenhouse gas emission was bi-directional with urbanization in America and trade in Europe. The bi-directional causality of trade was observed with financial development in Asia and urbanization in Africa. The generalized method of moment and fully modified ordinary least square explored a direct association of per capita greenhouse gas emission with financial development in Europe; energy consumption in all panels, and renewable energy in Africa and America. But, the relationship of per capita emission was reciprocal with financial development in Asia, Africa and America; renewable energy in Europe; trade in Europe, Africa and America; and urbanization in Asia, Europe and America. The relationship of variables changes from region to region and country to country. A country-based reciprocal effect on per capita greenhouse gas was observed for financial development, renewable energy, trade openness and urbanization for different countries. This paper provides an insight to policy makers to re-structure the financial system, energy usage pattern, trade and urbanization in accordance with the cleaning of environment at country and regional level. 
## 
## Topic 2: Economy
## V20: 10.1016/j.jclepro.2017.07.086
## This study is the first attempt to explore the impact of per capita renewable energy consumption and agricultural value added on carbon dioxide (CO2) emissions in four selected countries of the Association of Southeast Asian Nations (ASEAN-4: Indonesia, Malaysia, the Philippines, and Thailand) from 1970 to 2013. By examining the existence of the environmental Kuznets Curve (EKC) hypothesis, our results of long-run estimates do not support the inverted U-shape EKC in the selected countries. The estimates indicate that increasing renewable energy and agriculture decreases CO2 emissions, while non-renewable energy is positively correlated to emissions. Short-run Granger causal relationships exist from non-renewable energy to emissions and to agriculture, from economic growth to agriculture, and from agriculture to renewable energy directly. Long-run causalities indicate the existence of feedback causalities between emissions, renewable energy and non-renewable energy. Our policy implication is that developing sustainable agriculture can promote renewable energy and reduce emissions. 
## 
## Topic 2: Economy
## V21: 10.1016/j.energy.2017.11.092
## This study is the first attempt to investigate the nexus among per capita carbon dioxide (CO2) emissions, gross domestic product (GDP), and natural gas and renewable energy consumption within the framework of the environmental Kuznets curve (EKC), in a 1985–2016 sample of BRICS countries (i.e., Brazil, Russia, India, China, and South Africa). For this purpose, panel unit root, cointegration, and causality tests allowing for cross-sectional dependence are conducted. Employing the augmented mean group (AMG) estimator, the results provide strong evidence in favor of the EKC hypothesis for the BRICS countries by proposing that the EKC holds in all five BRICS countries; the turning points (TPs) lie between $4282.90 (India) and $16,553.77 (China), while the turning years (TYs) are estimated to be between 2018 (Russia) and 2035 (India). Furthermore, increasing natural gas and renewable energy consumption lowers CO2 emissions, which indicates that, 1% increase in natural gas and renewable energy consumption for the BRICS countries will decrease CO2 emissions by 0.1641% and 0.2601%, respectively. The causality analysis underscores feedback hypothetical links among CO2 emissions, natural gas consumption, and renewable energy consumption in the long run. Some policy implications are highlighted for BRICS countries&#39; policymakers not only for tackling CO2 emissions, but also for promoting growth in the natural gas and renewable energy industries. 
## 
## Topic 2: Economy
## V22: 10.1016/j.apenergy.2011.01.065
## This paper investigates the short-run and long-run causality issues between electricity consumption and economic growth in the selected 11 Middle East and North Africa (MENA) countries by using Autoregressive Distributed Lag (ARDL) bounds testing approach of cointegration and vector error-correction models. It employs annual data covering the period from 1971 to 2006. The unit root tests results indicate that some of the variables for Algeria, Jordan, Tunisia and United Arab Emirates do not satisfy the underlying assumptions of the ARDL bounds testing approach of cointegration methodology before proceeding to the estimation stage. Thus, we drop these countries from the ARDL bounds testing approach of cointegration and causality analysis. The cointegration test results show that there is no cointegration between the electricity consumption and the economic growth in three of the seven countries (Iran, Morocco and Syria). Thus, causal relationship cannot be estimated for these countries. However, the cointegration and causal relationship is found in four countries (Egypt, Israel, Oman and Saudi Arabia). The overall results indicate that there is no relationship between the electricity consumption and the economic growth in most of the MENA countries. Further evidence indicates that policies for energy conservation can have a little or no impact on economic growth in most of the MENA countries. © 2011 Elsevier Ltd. 
## 
## Topic 2: Economy
## V23: 10.1016/j.rser.2020.110094
## This paper applies a new bootstrap Autoregressive Distributed Lag (ARDL) approach to examine the nexus between trade openness, renewable electricity consumption, and economic growth for seven countries in the Asia-Pacific region during 1980–2017 period. In fact, there is no evidence of cointegration among real trade openness, electricity consumption, and real GDP per capita in countries, with the exception of Malaysia, where real GDP serves as dependent variable. However, cointegration does manifest in the cases of Indonesia, Japan, South Korea, and Thailand when trade openness is used as the dependent variable. A similar outcome is observed for Indonesia, Malaysia, and Thailand when renewable electricity consumption is used as the dependent variable. A short-run analysis reveals mixed results in term of the direction of the causality among different variables for various countries. Such results have implications for trade, energy, and environmental policies. One implication is that any environmental policy aiming to reduce the use of non-renewable energy and carbon dioxide emissions will inevitably lead to greater renewable energy consumption, which in turn may enhance trade openness and ultimately accelerate economic growth. 
## 
## Topic 2: Economy
## V24: 10.1002/sd.2400
## Sustainable development goals are one of the key targets to achieve for many countries across the globe. The current study aims to determine the impact of economic development and global commerce, specifically by considering trade separately, energy efficiency, and environmental levies on consumption-based carbon emissions (CCO2) for the G-7 countries between 1990 and 2020. This research used slope-heterogeneity and cross-section dependency to determine the unit root order. To assess the long run and short-run correlations between variables, the cross-sectionally augmented autoregressive distributed lags model (CS-ARDL) is employed, along with an augmented mean group test to ensure robustness. The study found that economic growth and imports are deteriorating, impacting the CCO2 emission of the G-7 countries, while the exports, energy efficiency and environmental taxes are having a corrective impact on the CCO2 emission. As per the results, focusing on energy efficiency will have a more correcting impact on CCO2 than on the other factors. The augmented mean group model further validates the results. 
## 
## Topic 2: Economy
## V25: 10.1016/j.energy.2013.07.067
## This study investigates the impact of the financial development on energy consumption in the GCC (Gulf Cooperation Council) countries. The panel data methodology was utilized taking the period 1980-2009. By using Pedroni cointegration test, it was found that LEC (energy consumption), the LFD (financial development), GDP (LGDP), LUR (urbanization) and LTD (total trade) are cointegrated. Furthermore, the panel dynamic OLS (ordinary least squares) revealed that LFD, LGDP, LUR and LTD have a long run positive effect on LEC. The Granger causality results show a bi-directional positive relationship between LEC and LGDP, LFD and LGDP, LTD and LGDP, LTD and LFD, LTD and LUR and between LTD and LTD. Inaddition, a one way positive causal relationship was found from LFD to LEC and from LUR to LEC. The study revealed that the financial development is one of the factors that increased energy consumption in the GCC in the short and the long run. From the findings of this study, a number of recommendations were formulated regarding the GCC policy makers to help reducing their energy consumption © 2013 Elsevier Ltd. 
## 
## Topic 3: Implementation
## V1: 10.1162/glep_a_00647
## In this forum, we highlight a discord in strategies around climate change policy and politics. On one hand, there is widespread concern for the pursuit of climate policy stability: stability in the design of policy and institutions, but particularly making policy and institutional development irreversible. However, much recent literature has revived an insistence on the inevitability of political conflict for pursuing the often large transitions needed to mitigate and adapt to accelerating climate change. Here, addressing climate change requires conflict, to weaken the power of incumbent actors that have blocked ambitious climate policy enactment for decades. Scholarship deploying each perspective tends to explicitly accept the need for radical sociotechnical transformations to address the climate crisis, but each entails radically different approaches to how to pursue decarbonization. The article outlines a research agenda focused on thinking about how these two apparently contradictory dynamics in climate politics interact, to advance our understanding of what sorts of strategies might open up political space for rapid transformations. 
## 
## Topic 3: Implementation
## V2: 10.1016/j.enpol.2012.08.045
## What explains the galvanising of communities to participate actively in energy projects? How do groups mobilize to overcome the often formidable barriers highlighted in the existing literature? Drawing on original qualitative research of 100 community energy groups in Scotland, including six in-depth case studies, we explain how effective mobilization occurs and the political dynamics surrounding such mobilization. To capture these dynamics, we adapt theories offered by literature on social movements, with a particular focus on resource mobilization theories. Applying our adapted framework, we identify two particular sets of resources shaping community energy mobilization: (i) structural resources, which refer to the broad political context structuring and constraining opportunities for community energy mobilization; and (ii) symbolic resources-less tangible resources used to galvanise participants. We investigate to what extent our case study groups were able to draw upon and exploit these resources. We find that structural resources can either facilitate or hinder mobilization; what matters is how state resources are exploited and constraints mitigated. The use of symbolic resources was highly effective in aiding mobilization. Each of the groups examined - despite their considerable variation - effectively exploited symbolic resources such as shared identity or desire for strong, self reliant communities. © 2012 Elsevier Ltd. 
## 
## Topic 3: Implementation
## V3: 10.1016/j.erss.2016.08.016
## The recent debate on the temporal dynamics of energy transitions is crucial since one of the main reasons for embarking on transitions away from fossil fuels is tackling climate change. Long-drawn out transitions, taking decades or even centuries as we have seen historically, are unlikely to help achieve climate change mitigation targets. Therefore, the pace of energy transitions and whether they can be sped up is a key academic and policy question. Our argument is that while history is important in order to understand the dynamics of transitions, the pace of historic transitions is only partly a good guide to the future. We agree with Sovacool&#39;s [1] argument that quicker transitions have happened in the past and may therefore also be possible in the future globally. The key reason for our optimism is that historic energy transitions have not been consciously governed, whereas today a wide variety of actors is engaged in active attempts to govern the transition towards low carbon energy systems. In addition, international innovation dynamics can work in favour of speeding up the global low-carbon transition. Finally, the 2015 Paris agreement demonstrates a global commitment to move towards a low carbon economy for the first time, thereby signalling the required political will to foster quick transitions and to overcome resistance, such as from incumbents with sunk infrastructure investments. 
## 
## Topic 3: Implementation
## V4: 10.1016/j.erss.2020.101485
## Is populist politics a threat to ambitious climate action and decarbonisation of energy systems? With the coinciding challenges of an alleged global ‘populist wave,’ and the growing visibility of a planetary climate crisis, this issue has gained relevance. While theoretical literature on the links between populism and climate action is growing, there is still little empirical evidence regarding the ways in which the thin ideology of populism can interact with climate policy in practice (particularly in the context of Eastern Europe). We look into this black box, by analyzing the Polish right-wing populist media discourses on energy and climate. Poland is widely perceived as a laggard in European climate policy and energy transition, seeking to safeguard domestic coal as a major energy source and opposing ambitious decarbonisation goals. At the same time, since 2015 it has seen a right-wing populist government in power, making it a very interesting case for an analysis of the interaction of populist politics and climate action. Using four key elements of populist political rhetoric as a framework (a populist episteme, Manichean good and evil connotations and internal enemies) we organize the arguments distilled from a wide reading of pro-governmental right-wing Polish media, to illustrate the content of populist climate and energy discourse, seeing some of the tropes found in our case as generalizable to other national contexts. 
## 
## Topic 3: Implementation
## V5: 10.1016/j.erss.2020.101871
## Empowering communities to engage with the energy transformation is vital to meet climate change mitigation and sustainable development goals. Communities have a key role to play in the energy transformation, through the uptake of new technologies and changing how they engage with energy. However, they remain marginalised limiting the broader benefits of meaningful participation such as energy democracy, energy justice and energy citizenship, which may be achieved through transformation. Addressing this problem requires an understanding of how to facilitate empowerment. Using a multidisciplinary approach, this paper synthesises the literature on community empowerment for sustainable transformations. It outlines a conceptualisation of empowerment through its definition, drivers and outcomes. Community empowerment in the energy transformation should be about the community&#39;s own goals and transformative action. This view of empowerment can be enabled through a variety of different drivers. Empowerment is identified as a multifaceted concept associated with a range of different outcomes such as participation, agency, autonomy and power-shift. Whilst the four outcomes identified in this review are typically described in isolation; we suggest that they are instead a scale wherein each step is a form of empowerment in its own right and yet represents an overall trajectory of empowerment. Facilitating community empowerment within the energy transformation requires a shift in both research and practice. 
## 
## Topic 3: Implementation
## V6: 10.1016/j.erss.2021.102146
## This paper takes up the challenge set down by the review work of Hess and Sovacool (2020) and Sovacool et al. (2020) and joins the conversation about future research agendas where STS is aligned towards humanities and social science research of energy solutions. We identified two under-representations in these review papers: 1) New materialism and object-oriented ontological (OOO) approaches and 2) how fictive imaginaries develop the link between OOO and public engagement with energy challenges. We propose that ontology of objects and non-human worlds is central to cocreation work in energy research where there exist assemblages of the Anthropocene. We argue that an ethical, engaged, object-oriented ontology that links with fictive imaginaries is crucial whichever direction STS takes in energy research. 
## 
## Topic 3: Implementation
## V7: 10.1016/j.erss.2020.101548
## Support to energy, particularly hydropower, has formed an important element of many donor programmes. How have such interventions shaped the emergence of particular energy imaginaries in the countries engaged with? ‘Energy imaginaries’ can be understood as the set of institutions, logics, values, and visions that spur ideas around what sources of energy and forms of energy governance best foster development. Adopting a historical and comparative perspective and drawing on the notion of ‘transnational assemblages’, we explore the nature of energy aid interventions and the dynamic shifts of specific actors and discourses in bilateral relations. We focus on Norway, a leading player in energy support, and two of its long-term partner countries, Nepal and Tanzania. Through document analysis and interviews with key actors, we trace how Norwegian energy transnational assemblages have formed part of evolving energy imaginaries in Nepal and Tanzania in radically diverging ways. In Nepal, dominated by an energy imaginary of hydropower as ‘white gold’, efforts to foster a bottom-up-driven indigent energy sector were eclipsed by an emphasis on facilitating privatisation, resulting in a chaotic fragmentation of the energy landscape. In Tanzania, the donor-state energy imaginaries were centred on grandiose projects of hydropowered industrialisation bound for failure, but later revived as part of an authoritarian project. The study untangles a history of changing and partially conflicting discourses, offering a richer and more nuanced understanding than studies focused on single projects or policies. We highlight how the idea of transnational assemblages can be useful in understanding shifting imaginaries of energy development. 
## 
## Topic 3: Implementation
## V8: 10.1016/j.enpol.2022.113277
## Community Wealth Building (CWB) is a burgeoning international policy agenda for local economic development that seeks to enhance democratic ownership, retain the benefits of local economic activity and empower place-based economies and workers. Parallel to this, in the context of net zero transitions, there has been increasing interest in approaches to enhancing civil society and community ownership over local energy provision. However, in academic and practitioner debates, there has been very little interaction between these two strands of thinking and action on the need for radical change in current energy provision, particularly as part of a wider transformative change away from dominant neoliberal economic thinking, policies and structures. In this Perspective, we explore the various ways in which synergies exist between CWB and energy transitions by considering two civil society approaches to transitions; namely, the Thousand Flowers transition pathway and research in Grassroots Innovations. We examine how community energy could be strengthened through CWB, by showing how the ideas within these two approaches respond to the five core principles of CWB. Promising future directions for research and practice are identified, including linking up CWB and just transitions strategies, a renewed focus on local financial innovation and the growing role of anchor institutions in supporting net zero transitions, particularly where CWB supports economic democracy transformations in new net zero economies. 
## 
## Topic 3: Implementation
## V9: 10.1016/j.erss.2016.12.003
## Science, Technology and Society (STS) research on the social dimensions of technology development emphasizes how imagined publics who play passive roles in technology systems influence technology design and public engagement. It further documents expert adherence to deficit models for conceptualizing publics’ responses to technology development. Building on this literature, this paper examines two related questions that have received little attention. First, how do experts conceptualize publics whose direct participation with technology development, rather than quiescence toward technology development, is framed by experts as mandatory for the technology project&#39;s success? Second, given both the prevalence and problematic outcomes of expert and institutional adherence to deficit models, what might an alternative model for conceptualizing publics look like? To address these questions, we draw from a case study of 30 “bioenergy experts” in the Northeastern U.S. and their imagined landowning publics. Our analysis provides two contributions. First, we develop the concept “obligatory publics” to examine expert imaginaries of publics whose direct participation is seen as mandatory for successful renewable energy technology (RET) development. And second, we suggest a “hermeneutic-dialectic” approach, which emphasizes the role of dialogue between experts and publics for achieving new and mutually agreed upon understandings, as one possible alternative to public deficit models. 
## 
## Topic 3: Implementation
## V10: 10.1016/j.erss.2022.102509
## This article shows how energy transitions are advanced by energy elites – the leaders and experts of energy companies. While the dominant literature suggests that elites resist societal changes, this research highlights that energy elites are instrumental in the promotion of energy transitions. The findings in this article are based on 18 months of ethnographic fieldwork in energy companies based in Oslo, Norway, and analysed using anthropological perspectives. The research has found that energy elites, faced with climate change concerns, re-imagined energy futures and accordingly reoriented their careers. Out of a total of 109 energy elites interviewed, 30% decided to leave the hydrocarbon sector in pursuit of careers in renewables. As energy elites left their occupations in oil and gas, they inspired others – including elites and non-elites – to follow suit. This suggests that elites are not only crucial to the pursuit of energy transitions but can also be key pillars of promoting societal changes. What is more, the research demonstrates that elites do not operate in isolation. Factors like social networks, politico-economic contexts, and changes in investor climates all contribute to the promotion of low-carbon energy futures. This article provides a deeper understanding of the multifaceted drivers of energy transitions – in Norway and beyond. 
## 
## Topic 3: Implementation
## V11: 10.1016/j.rser.2019.109518
## Although the need for energy decentralization and energy democratization has been discussed in the realms of academe for some time, the impact at policy level is yet to be seen. This is however with the notable exception, at the European level, of the Recast Renewable Energy Directive (RED-II) which aims in part to stimulate the formation of ‘renewable energy communities’ in all the Member States. The implementation at policy level remains with Member States where national policies are still overwhelmingly centralised. This creates barriers to the decentralization and democratization of energy and, ultimately, to achieving a ‘just transition’. This article therefore proposes as a way forward to recognise such renewable energy communities as legal entities to be embedded within a separate socio-legal institution (of civil energy networks), to shape (a transition towards) a just new energy system. After explaining what form this new institutional environment and the communities within would take, this article analyses this proposition in the light of the twin aims of energy decentralization and democratization to see how such forms can help achieve energy justice. It is also explained why this can only work upon an institutionally normative alignment. Following an appreciation in the light of growing research of such forms in the Netherlands, the UK and further afield, there seems to be a strong case for the proposed model. Still, realization requires clear purposive legislative steps. Such steps can only lead to ‘on the ground’ success with a coherent and multi-disciplinary institutional perspective. We aim for our research to provide a platform for such efforts. 
## 
## Topic 3: Implementation
## V12: 10.1038/s41560-022-01093-8
## Rapid action is needed in the energy sector to respond to the climate emergency. Here we argue for the increased use of ‘realist’ approaches in sociotechnical energy studies to inform this action. Realist approaches ask not just ‘what works?’, but also ‘for whom, in what circumstances and why?’. They place emphasis on understanding the mechanisms by which outcomes of interventions (such as policies) come about and, crucially, how this depends on contextual factors. This can inform action based on existing learning from a wide range of sectors and disciplines—action that is tailored to be effective in specific contexts, and can respond to the potential for unjust outcomes. In this way they can support justice, interdisciplinary working, and urgency in energy research. We consider limitations and drawbacks of the approach (and responses to these), and present a guide to getting started. 
## 
## Topic 3: Implementation
## V13: 10.1016/j.erss.2014.02.001
## This paper addresses key implications in momentous current global energy choices - both for social science and for society. Energy can be over-used as a lens for viewing social processes. But it is nonetheless of profound importance. Understanding possible &#39;sustainable energy&#39; transformations requires attention to many tricky issues in social theory: around agency and structure and the interplay of power, contingency and practice. These factors are as much shaping of the knowledges and normativities supposedly driving transformation, as they are shaped by them. So, ideas and hopes about possible pathways for change - as well as notions of &#39;the transition&#39; itself - can be deeply constituted by incumbent interests. The paper addresses these dynamics by considering contending forms of transformation centring on renewable energy, nuclear power and climate geoengineering. Several challenges are identified for social science. These apply especially where there are aims to help enable more democratic exercise of social agency. They enjoin responsibilities to &#39;open up&#39; (rather than &#39;close down&#39;), active political spaces for critical contention over alternative pathways. If due attention is to be given to marginalised interests, then a reflexive view must be taken of transformation. The paper ends with a series of concrete political lessons. © 2014 The Author. 
## 
## Topic 3: Implementation
## V14: 10.1016/j.erss.2017.04.001
## There is a significant ‘action gap’ between what scientists argue is necessary to prevent potentially dangerous climate change and what the government, industry and public are doing. This paper argues that a coherent strategic narrative is key to making meaningful progress. It does this by first analysing a number of narratives which have been used to try and create audience buy-in on the need for action on climate change, and those that argue that no action needs to be taken. A framework is then proposed for how compelling and unifying strategic narratives on climate change might be constructed. It is suggested that the unifying strategic narrative could address the complex range of actors who need to be engaged, provide a coherent explanation for government strategy, and harness the drivers of behavioural change needed to meet the challenge. Research into climate change strategic narratives is nascent, but the authors believe that there is much to be gained from pursuing and intensifying this research. 
## 
## Topic 3: Implementation
## V15: 10.1016/j.eist.2023.100775
## Social innovation is gaining attention as a pivotal dimension of socio-technical transitions with renewable energy prosumerism as a prominent example. However, this example also highlights that social innovation evokes concerns about purposes, beneficiaries, normative dilemmas and legitimacy. This paper addresses recent calls to confront the perceived ‘dark sides’ of social innovations. As debates on these dark sides often get stuck in either naive optimism or paralyzing critique, the paper investigates how transitions theory can inform nuanced understandings. The key concept is transitions directionality. The analysis shows how it conceptualizes the dark sides as manifestations of socio-technical path dependence, as disempowering ideological ‘landscape’ factors, as internal contradictions within institutionally complex regimes, as niche-regime dialectics, and as transition phases. Rather than proposing a particular normative position, the paper presents a heuristic that supports well-considered engagement with the dark sides. 
## 
## Topic 3: Implementation
## V16: 10.1162/GLEP_a_00043
## Climate change adaptation presents a paradox: climate change is a global risk, yet vulnerability is locally experienced. Effective adaptation therefore depends on understanding the local context of vulnerability, which requires deliberative and participatory approaches to adaptation policy-making. But, how can local inclusiveness be achieved in the context of global environmental risk, and what sorts of institutions are needed? This article examines one avenue for the participation of vulnerable groups in adaptation policy-making: National Adaptation Programmes of Actions (NAPAs). Drawing on the case study of Bangladesh, this article shows that the &quot;adaptation paradox&quot; creates a tension between local and global definitions of climate change risk, affecting the legitimacy of participatory processes under the NAPA. I propose that early analysis and engagement of existing local institutional frameworks as a starting point for national adaptation planning is one possible entry point for meaningful local deliberation in global climate change policy-making processes. © 2011 by the Massachusetts Institute of Technology. 
## 
## Topic 3: Implementation
## V17: 10.1016/j.erss.2018.02.004
## Greenhouse gas emissions are stagnating in Germany despite increasing deployment of renewable energy. This makes the government&#39;s Energiewende appear inconsistent and has triggered a discussion on phasing-out coal. The focus has thus turned from niche technology development to the destabilization of the existing high-carbon regime. In this paper we investigate stakeholders’ framings and their perceptions of different policy options to advance the understanding of regime destabilization processes and theory-building in the context of the multi-level perspective (MLP) on socio-technical transitions. We find that actors still form coalitions with traditional allies and cling to established lines of reasoning, although there are indications for a beginning disintegration of the status quo-defending coalition. In their framings, core actors emphasize risks and threats. This confirms that regime destabilization is particularly conflictual and shows that for actors pushing regime change it is more difficult to offer a positive story. Linking policies for phasing-out incumbent technologies to accompanying measures for managing structural change in affected regions may facilitate compromise. The results moreover point to a tension between national and supra-national action as a core issue in destabilization debates. Our insights are relevant for countries in similar transition phases and may inform future comparative research. 
## 
## Topic 3: Implementation
## V18: 10.1016/j.eist.2021.12.005
## This contribution outlines how transition processes can be analysed as ‘transition work’. We draw on the concept of institutional work, review existing applications in transition research and adapt it to transitions, labelling the result ‘transition work’. Transition work details how actors ‘create’, ‘maintain’ and ‘disrupt’ transition processes. By analysing instances around the evolution of policies, organisations, networks and technologies in two cases of energy transitions in Germany, we observe how actors apply transition work to accelerate and decelerate transitions. Despite analysing very different cases, we find that creating, maintaining, and disrupting are all integral activities of transition processes. Particularly, we observe a lack of stabilisation of a transition process where ‘maintain’ activities of transition proponents are scarce: Maintain activities cannot only preserve the status quo, but also stabilise transition activities. For understanding disruption, likewise, we need to consider both disruptive and defensive work. 
## 
## Topic 3: Implementation
## V19: 10.1016/j.enpol.2012.03.040
## The discussion to widen access to modern energy services has been influential in shaping some of the discussions on energy at the international level. The practice of widening modern energy services access to the poor in Africa is complex, and exacerbated by the dual nature of the energy system across Sub-Saharan Africa where traditional and modern energy systems and practices co-exist. This presents major challenges for policy makers who have to contend with a fragmented energy system, which requires the mobilisation of an array of actors at cross-sectoral levels in order to develop effective institutions and implement innovative policy frameworks. This paper further argues that, the &#39;energy access&#39; discussion needs to take place in the context of energy transitions, giving due consideration to the productive sector as an important vehicle for change. As the link between energy and development is context specific, each African country needs to chart its own energy transition pathway into the future, and there are ample lessons that they can draw from previous energy transitions. © 2012 Elsevier Ltd. 
## 
## Topic 3: Implementation
## V20: 10.1016/j.enpol.2016.12.021
## Political debate on energy in Germany has been shaped by two historically opposed discourses, one pushing for a transition to renewables, the other holding on to the status quo. Scientific policy advice (SPA) has been involved in their evolution from the beginning. This paper draws on the Advocacy Coalition Framework and on discourse and narrative theory to study the role of SPA in recent German energy policy. We explore 1) whether scientific advisors have been members of advocacy coalitions, and 2) how their contributions may have interacted with the evolution of the discourses and major narratives. We perform a qualitative text analysis of 50 SPA reports published between 2000 and 2015. We find that the majority of studies clearly take sides in the debate, and that in most cases the reports’ positions are fully transparent. Despite the polarization, SPA provides differentiated information on key aspects of the discourses, and alternative design options for policy instruments. We conclude that SPA contributions have improved the conditions for political consensus and compromise. Collectively, SPA studies provide a basis for mapping different policy pathways and their consequences. In the future, SPA should address additional critical issues such as coal phase-out and international leadership. 
## 
## Topic 3: Implementation
## V21: 10.1016/j.erss.2020.101898
## The paper highlights shortcomings in the contribution of qualitative social sciences and humanities (SSH) research to tackling challenges connected with energy and climate change. These shortcomings are illustrated based on analysis of data gathered in relation to EU (e.g. Horizon 2020; FP7) and European national research funding and energy policy. The paper finds that a techno-economic energy imaginary continues to dominate European energy systems and governs expectations of energy research and its conduct, the integration of SSH with energy policy-making and the framing and foci of policy. A more nuanced, context-sensitive approach is presented as an alternative ‘practices and cultural change’ energy imaginary. This emphasises attention to social practices relevant to energy use, interdisciplinarity and the coproduction of knowledge with diverse actors. Adoption of such an imaginary can help to enhance policy integration of SSH and the contribution of SSH to ameliorating energy and climate change challenges while providing insight into why gaps occur between (supra)national energy policy and local practices. 
## 
## Topic 3: Implementation
## V22: 10.1016/j.erss.2015.12.008
## This article examines how members of the Swedish Parliament framed nuclear energy in the 2010 debate on the future of nuclear power in Sweden in order to understand how politicians construct and contextualize their views on the role of nuclear energy in energy transitions. Our findings suggest that four themes could be identified in the debate and that these were formative for politicians in framing nuclear energy. Even though all political actors anticipate an energy transition towards a more sustainable system, different paths to advancing in this process were brought up in the debate, both with and without prolongation of the nuclear energy program. Our analysis suggests that framings of nuclear energy are closely related to the political ideologies of the parties in the Parliament because the two framings of nuclear energy correspond with the division of the Swedish Parliament into two political blocs. However, views on nuclear energy are not inherent to political ideologies but are constructed. This article thus integrates the politics of nuclear energy within the research on energy transitions. 
## 
## Topic 3: Implementation
## V23: 10.1016/j.enpol.2010.06.037
## A broad participation by stakeholders and an extensive reliance on expert advice are often seen as preconditions for a legitimate and successfully implemented renewable energy policy. However, we have lacked systematic data for testing this argument. This article&#39;s contribution is to examine the actors who take part in the making of Swedish energy policy with the help of data on the composition of various committees of inquiry over the last twenty years (1988-2009). Swedish renewable energy policy is often characterised with words like &quot;pioneering&quot; and &quot;forerunner&quot;, suggesting that the policy-making process in this area engages many different experts and stakeholders. Our data give only some support to this argument. Results point to a noteworthy predominance of politicians, civil servants, and representatives of state agencies within the policy-process. Producers of uranium and fossils based energy have been engaged more often than producers of renewable energy. Experts have played a prominent role, but this is mostly due to the participation of expert bureaucrats rather than of scientists. The study suggests that a better understanding of the making of energy policy, both in Sweden and elsewhere, requires greater attention to the networks and role of various state employees. © 2010. 
## 
## Topic 3: Implementation
## V24: 10.1016/j.erss.2021.102164
## Building from two strands of literature within “Sociotechnical agendas: Reviewing future directions for energy and climate research”, this perspective piece seeks to open a discussion about how to responsibly accelerate transitions. First, we identify a managerial literature on how innovation and diffusion can be accelerated, which focuses on deliberation with consensus-oriented ambitions. Second, is a set of perspectives that highlights unevenness, and therefore seeks to radically expand climate and energy democracy by promoting new forms of participatory practices. There are few contact points between these literatures. We argue that this can be explained by tensions and paradoxes that accompany accelerated transitions. These paradoxes cannot easily be resolved. We discuss how accelerated social and technological change poses challenges to inclusive and participatory transitions. Further, we discuss how rapid transitions tends to contribute to conflicts between core and peripheral sites. Thus, transitions are not only affected by societal conditions, but also contribute to co-producing social order, including the many forms of social turmoil currently experienced. Such insight places greater responsibilities on transition scholars, especially from reflexive disciplines such as STS and geography. We conclude by discussing how this responsibility could translate into situating climate and energy transition scholarship in broader debates about future socio-technical orders, and sketch three principles of responsible acceleration: a) Moving towards a broad epistemic basis for producing and assessing transition policies and strategies, b) Nurturing polycentrism for rapid climate and energy transitions, and c) Developing polytemporal strategies for transition. 
## 
## Topic 3: Implementation
## V25: 10.1162/glep_a_00652
## What can an “ethnographic sensibility” contribute to research on climate change governance? With its emphasis on meaning making and understanding what may lie beneath more obvious interactions and processes, ethnographic methodologies, particularly collaborative event ethnography, are increasingly deployed to address complex questions and achieve conceptual leverage on issues related to climate governance. Drawing on literature in climate anthropology, material geography, and political ethnography, and with examples from our own fieldwork experiences, we devise a heuristic typology underpinned by an ethnographic sensibility to help guide the fieldwork phase of a research project. Building on the well-established practice of hanging out, we introduce hanging around, which attends to spatiality and matter; hanging in, which addresses issues of access and trust; and hanging back to guide the practice of reflexivity. We articulate what fieldwork with an ethnographic sensibility entails and discuss its potential and implications for climate governance research. 
## 
## Topic 4: Biomass
## V1: 10.1016/j.coal.2019.03.006
## Three different lithotypes (xylitic, gelified and matrix) of Pliocene lignite from the Velenje Basin, Slovenia, were investigated to establish the variations of biomarker compositions in solvent extracts and the stable isotope (carbon and nitrogen) compositions of bulk material. From the biomarker results, the xylitic lithotype almost exclusively originates from gymnosperms (conifers such as Taxodiaceae), as indicated by the very high contents of sesquiterpenoids and diterpenoids but very low abundances of n-alkanes and non-hopanoid triterpenoids. The relative proportion of gymnosperms to angiosperms in the paleomire is reflected by the ratio of diterpenoids to the sum of diterpenoids and non-hopanoid triterpenoids (Di-/(Di-+Tri-terpenoids)), which is close to 1 (av. 0.99) in the xylitic lithotype. The predominance of diterpenoids from conifers in the xylitic lithotype is associated with high C/N ratios and intermediate total sulfur (TS). The very low abundance of hop-17(21)-ene and the absence of further hopanoids in the xylitic lithotype indicate a restricted influence of bacterial degradation under relatively dry conditions in the paleomire. The matrix lithotype also originated preferentially from gymnosperms under dry depositional conditions, as indicated by the high Di-/(Di-+Tri-terpenoids) ratio (0.95), low amounts of hopanoids and low TS content. The gelified lithotype is characterized by a high content of n-alkanes and wide variation of the Di-/(Di-+Tri-terpenoids) ratio (0.13–0.88), indicating a fluctuating contribution of angiosperms to the plant community in the paleomire during formation of this lithotype. In addition, the high abundance of hop-17(21)-ene and TS in the gelified lithotype compared with the other two lithotypes suggests the effect of bacterial activity under relatively wet/humid conditions during formation of the gelified lithotype, which is also supported by the considerable content of mid-chain n-alkanes. The high correlation between the δ 13 C and δ 15 N values (R 2 = 0.68) indicates that the stable carbon and nitrogen isotope composition in the Velenje lignites were probably influenced by the same factors (e.g. precursor plants and/or microbial activity). The stable carbon isotopic values (av. −25.44‰) and nitrogen isotopic values (av. 2.15‰) of the xylitic lithotype are higher than those of the gelified lithotype (av. δ 13 C = −27.48‰ δ 15 N = 1.37‰) and the matrix lithotype (av. δ 13 C = −27.09‰ δ 15 N = 1.10‰). The relatively high correlation between the diterpenoid content and both δ 13 C and δ 15 N values suggests that the stable carbon and nitrogen isotopic composition of the three lithotypes might reflect the composition of the original plant material in the paleomire. The dominance of conifers as precursor plants in the xylitic lithotype might be the main reason for the higher δ 13 C values and probably also the higher δ 15 N values. The relatively higher δ 15 N values in the xylitic lithotype than in the other types could be explained by the high amount of decay-resistant xylem and low mineral (e.g. clay) content in the xylitic lithotype. The slightly lower δ 13 C but higher δ 15 N values in the gelified lithotype than in the matrix lithotype can be explained by variation of parent plant materials and the influence of bacterial activity. 
## 
## Topic 4: Biomass
## V2: 10.1186/s13068-016-0489-y
## Background: The lignocellulose biorefinery based on the sugar platform usually focuses on polysaccharide bioconversion, while lignin is only burned for energy recovery. Pyrolysis can provide a novel route for the efficient utilization of residual lignin obtained from the enzymatic hydrolysis of lignocellulose. The pyrolysis characteristics of residual lignin are usually significantly affected by the pretreatment process because of structural alteration of lignin during pretreatment. In recent years, biological pretreatment using white-rot fungi has attracted extensive attention, but there are only few reports on thermal conversion of lignin derived from enzymatic hydrolysis residue (EHRL) of the bio-pretreated lignocellulose. Therefore, the study investigated the pyrolysis characteristics and kinetics of EHRL obtained from bamboo pretreated with Echinodontium taxodii in order to evaluate the potential of thermal conversion processes of EHRL. Results: Fourier transform infrared spectroscopy spectra showed that EHRL of bamboo treated with E. taxodii had the typical lignin structure, but aromatic skeletal carbon and side chain of lignin were partially altered by the fungus. Thermogravimetric analysis indicated that EHRL pyrolysis at different heating rates could be divided into two depolymerization stages and covered a wide temperature range from 500 to 900 K. The thermal decomposition reaction can be well described by two third-order reactions. The kinetics study indicated that the EHRL of bamboo treated with white-rot fungus had lower apparent activation energies, lower peak temperatures of pyrolysis reaction, and higher char residue than the EHRL of raw bamboo. Pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) was applied to characterize the fast pyrolysis products of EHRL at 600 °C. The ratios of guaiacyl-type to syringyl-type derivatives yield (G/S) and guaiacyl-type to p-hydroxy-phenylpropane-type derivatives yield (G/H) for the treated sample were increased by 33.18 and 25.30 % in comparison with the raw bamboo, respectively. Conclusions: The structural alterations of lignin during pretreatment can decrease the thermal stability of EHRL from the bio-treated bamboo and concentrate the guaiacyl-type derivatives in the fast pyrolysis products. Thus, the pyrolysis can be a promising route for effective utilization of the enzymatic hydrolysis residue from bio-pretreated lignocellulose. 
## 
## Topic 4: Biomass
## V3: 10.1016/j.petrol.2021.109972
## The Papuan Basin is the largest petroliferous basin in Papua New Guinea (PNG), where developed several sets of potential source rocks (SRs). The Jurassic SRs are clay-rich shales, whose organic matter (OM, mainly type Ⅱ2-Ⅲ) is predominantly derived from terrestrial higher plants deposited in a more oxic environment with relatively high maturity (0.35% &lt; Ro &lt; 2.03%). The Cretaceous SRs contain mixed inputs of terrigenous and marine OM (type Ⅱ2-Ⅲ) formed in sub-oxidized to oxidized depositional conditions with moderate maturity (0.33% &lt; Ro &lt; 1.58%). Whereas the Paleocene-Miocene SRs are predominant marine calcareous shales with subsidiary terrigenous OM inputs. These SRs containing type Ⅲ kerogen are mostly immature (0.21% &lt; Ro &lt; 0.70%). The Papuan Basin oils were classified into three distinct genetic families based on biomarkers and stable carbon isotopes. Family A oils, with heavy carbon isotopes, low Pr/Ph ratios, C29/C27 ααα 20 R sterane &lt;1.0, are likely originated from autochthonic Paleogene-Neogene mature marine OM. Family B oils have the characteristics of high Pr/Ph, moderate carbon isotopes, the dominance of C29 ααα 20 R sterane, and are predominantly derived from autochthonous Jurassic SRs. Family C oils in the Eastern Papuan Fold Belt (EPFB) can be further subdivided into three subtypes by more subtle differences in specific biomarkers. Type C1 oils, most likely stemmed from the Lower Cretaceous SRs, are characterized by light stable carbon isotopes, Pr/Ph &gt; 2, the mild dominance of C29 ααα 20 R sterane and the absence of oleanane ± lupane. Type C2 oils can be clearly distinguished from Type C3 by C19/(C19 + C23) tricyclic terpane &lt;0.5, while the latter ones have higher abundances of oleanane ± lupine and diahopanes as well as C24 tetracyclic/C23 tricyclic terpane ratios. Type C2-3 oils are originated from calcareous SRs that deposited in Late Cretaceous or younger age. 
## 
## Topic 4: Biomass
## V4: 10.1186/s13068-018-1345-z
## Background: Raw-starch-digesting glucoamylases (RSDGs) from filamentous fungi have great commercial values in starch processing; however, the regulatory mechanisms associated with their production in filamentous fungi remain unknown. Penicillium oxalicum HP7-1 isolated by our laboratory secretes RSDG with suitable properties but at low production levels. Here, we screened and identified novel regulators of RSDG gene expression in P. oxalicum through transcriptional profiling and genetic analyses. Results: Penicillium oxalicum HP7-1 transcriptomes in the presence of glucose and starch, respectively, used as the sole carbon source were comparatively analyzed, resulting in screening of 23 candidate genes regulating the expression of RSDG genes. Following deletion of 15 of the candidate genes in the parental P. oxalicum strain ΔPoxKu70, enzymatic assays revealed five mutants exhibiting significant reduction in the production of raw-starch-digesting enzymes (RSDEs). The deleted genes (POX01907, POX03446, POX06509, POX07078, and POX09752), were the first report to regulate RSDE production of P. oxalicum. Further analysis revealed that ΔPOX01907 lost the most RSDE production (83.4%), and that POX01907 regulated the expression of major amylase genes, including the RSDG gene POX01356/PoxGA15A, a glucoamylase gene POX02412, and the α-amylase gene POX09352/Amy13A, during the late-stage growth of P. oxalicum. Conclusion: Our results revealed a novel essential regulatory gene POX01907 encoding a transcription factor in controlling the production of RSDE, regulating the expression of an important RSDG gene POX01356/PoxGA15A, in P. oxalicum. These results provide insight into the regulatory mechanism of fungal amylolytic enzyme production. 
## 
## Topic 4: Biomass
## V5: 10.1016/j.coal.2020.103558
## The present research compares the yield of microbial methane production from different lithotypes of Miocene lignite from the Konin Basin in Poland and attempts to establish an understanding of the processes responsible for methanogenesis. A series of batch experiments were carried out with detritic and xylitic lignites inoculated with microorganisms from an external source (the anaerobic chamber of a wastewater treatment plant in a sugar factory). Biogas volume, concentration and stable carbon isotopes of microbial methane were measured. It was found that detritic lignites are a slightly more suitable raw material for microbial methane production than xylites. Methane yield for detritic lignites equalled 14.3 μmol CH4/g of total organic carbon (TOC); for xylites, 13.7 μmol CH4/g of TOC. The mean δ13C(CH4) value in experiments with detritic coal from Konin equalled −36.3‰; for fossil wood fragments, −47.3‰ and −42.7‰. We suppose that differences in mean δ13C(CH4) values from the biodegradation of different lithotypes of lignite from the Konin Basin most probably depend on cellulose and lignin content (%). The holocellulose content in xylites decreased over time, suggesting the important role of the enzymatic hydrolysis of cellulose to glucose. Methane from detritic lignite was formed due to lignin decomposition. Methane-producing microbial communities were dominated by Bacteria mainly from the phyla Proteobacteria (Alpha-, Beta- and Gamma- or Deltaproteobacteria), Firmicutes (Clostridia and Bacilli), Actinobacteria, and Bacteroidetes. Archaea constituted only 2–6% of the microbial community, including Methanosarcinales, Methanomicrobiales, and Methanobacteriales. The data presented here show no clear correlations between lignite type and specific bacterial or archaeal taxa. However, certain tendencies were observed. Exiguobacterium and Pseudomonas are more abundant in xylites, Rhizobacteriaceae in detritic lignites. This indicates the complexity and diversity of lignite material and processes leading to lignite degradation. In the presented research model, anaerobic oxidation of methane may occur. 
## 
## Topic 4: Biomass
## V6: 10.1016/j.biortech.2019.01.099
## Biomethanation of rice straw was performed at 55 °C without thermochemical pretreatment using cattle dung supplemented with Methanothermobacter thermautotrophicus strains. Methane yield of 323 ml g−1 VS obtained under optimized conditions such as particle size (1 mm), carbon to nitrogen ratio (15:1), substrate to inoculum ratio (1:1), organic loading rate (7.5% w/v) and hydraulic retention time (20 days), was one of the highest ever reported from rice straw. Metagenome analysis revealed several putative novel taxa among resident microbes. The genomes of Clostridium, Hungateiclostridium, Alkaliphilus, Anaerocolumna, Olsenella, Paenibacillus, Pseudoclostridium, Tepidanaerobacter and Turicibacter were recovered as metagenome assisted genomes. Clostridium spp. and M. thermautotrophicus were the dominant hydrolytic and methanogenic microbes, respectively. Syntrophic acetate oxidation coupled to hydrogenotrophic methanogenesis was found to be the major pathway for methane production. Efficient thermophilic biomethanation of rice straw without thermochemical pretreatment using cattle dung supplemented with M. thermautotrophicus is reported for the first time. 
## 
## Topic 4: Biomass
## V7: 10.1016/j.renene.2023.119029
## Arginine repressor (ArgR1864) is a multifunctional regulator in Thermoanaerobacterium aotearoense SCUT27 (SCUT27). For better understanding its function in SCUT27, the transcriptome of SCUT27/ΔargR1864 under xylose was further investigated. 2014 (V518_2014) denoted as a hypothetic protein gene was mined, which played important roles on xylose metabolism and carbon metabolic flux distribution in SCUT27. In 2014 deletion mutant, no lactic acid could be produced along with enhanced ethanol formation. Same changes were also observed in SCUT27/ΔargR1864. ArgR1864 is the direct regulator of 2014 identified by electrophoretic mobility shift assays (EMSA). When 2014 was complemented in SCUT27/ΔargR1864, the fermentation traits of SCUT27/ΔargR1864/2014 were returned to wild-type levels, suggesting that the improved xylose utilization and ethanol formation of SCUT27/ΔargR1864 were attributed to the low expression of 2014. When cultured under sorghum stalk, corn cob and wheat straw hydrolysates, SCUT27/Δ2014 also showed the excellent capability for sugar utilization and ethanol production with the consumption rate of xylose and glucose increased by 20%–37.93 and 64.29%–190.91% separately, the ethanol production improved by 326.61%–547.89% and ethanol yield enhanced by 216.67%–337.50%. Thus, 2014, as a hypothetic protein gene, could be used as the new genetic reference to predict functional genes to engineer the microorganism for biofuel production from lignocellulosic hydrolysates. 
## 
## Topic 4: Biomass
## V8: 10.1016/j.renene.2013.02.035
## Biodiesel is an environmental-friendly fuel that can replace petroleum diesel. However, after transesterification reaction of vegetable oils, the obtained crude biodiesel must be purified. The commonly applied purification step is water washing. This step is a concern in biodiesel production, since large quantities of clean water are used, generating a wastewater stream to be further treated. Here we propose the application of microand ultrafiltration processes to purify crude biodiesel. Crude biodiesel was filtrated in a dead-end process at different transmembrane pressures and using membranes of different pore sizes. Flux results showed that greater transmembrane pressures, as well as greater pore sizes, enablegreater fluxes. Density, viscosity and acid values of purified biodiesel (washed and filtrated) are in accordance to the international legislation for biodiesel quality. Both processes (water washing and membrane separation) were able to reduce the amount of soap detected in crude biodiesel. However, the proposed microfiltration membranes were not efficient as the washing method to reduce the free glycerol content. The ultrafiltration membrane of 30kDa was also not able to produce a purified biodiesel according to the international legislation for free glycerol content. Between the analyzed membranes, the glycerol content limit (less than 0.02wt%) was achieved only with the ultrafiltration membrane of 10kDa. Water addition in the crude biodiesel improved the glycerol removal by membrane filtration. The obtained results showed that the membrane separation process is a suitable alternative for biodiesel purification. © 2013 Elsevier Ltd. 
## 
## Topic 4: Biomass
## V9: 10.1111/gcbb.12161
## Fertilization effects and risks of heavy metal enrichment were studied in a field experiment, in which plots of reed canary grass (RCG) were treated annually with three different fertilizers: Ash from co-combustion of RCG and municipal wastes (mixed ash), pure RCG ash, and commercial fertilizer (control). RCG ash is a waste product that is currently expensive to dispose of. The amounts of nutrients applied annually were 100 kg ha-1 N, 15 kg ha-1 P, and 80 kg ha-1 K in all treatments. In the ash treatments, all P derived from ash, whereas N and part of the K were supplemented by fertilizers. The amount of heavy metals exceeded the limits set by the Swedish Environmental Protection Agency for all elements analyzed in the mixed ash and for Ni and Cr in the RCG ash. There were no significant differences between treatments in terms of RCG dry matter yield obtained at harvest in spring, or in heavy metal concentrations in the biomass. Soil samples from 0-5 cm, 5-10 cm, and 10-20 cm below the surface showed significant differences between treatments for the concentration of Cd, Cr, Cu, Pb, and Zn, with higher concentrations in plots fertilized with mixed ash than in the control. Neither spring yield nor soil available P was reduced by using ash instead of mineral P fertilizer, suggesting that pure RCG ash can be used to complement commercial fertilizer, albeit less frequently than here. However, ash derived from co-combusting RCG with different waste materials (mixed ash treatment) should not be used in RCG production due to the high heavy metal content. 
## 
## Topic 4: Biomass
## V10: 10.1186/1754-6834-4-20
## Background: The use of energy crops and agricultural residues is expected to increase to fulfil the legislative demands of bio-based components in transport fuels. Ensiling methods, adapted from the feed sector, are suitable storage methods to preserve fresh crops throughout the year for, for example, biogas production. Various preservation methods, namely ensiling with and without acid addition for whole crop maize, fibre hemp and faba bean were investigated. For the drier fibre hemp, alkaline urea treatment was studied as well. These treatments were also explored as mild pretreatment methods to improve the disassembly and hydrolysis of these lignocellulosic substrates. Results: The investigated storage treatments increased the availability of the substrates for biogas production from hemp and in most cases from whole maize but not from faba bean. Ensiling of hemp, without or with addition of formic acid, increased methane production by more than 50% compared to fresh hemp. Ensiling resulted in substantially increased methane yields also from maize, and the use of formic acid in ensiling of maize further enhanced methane yields by 16%, as compared with fresh maize. Ensiled faba bean, in contrast, yielded somewhat less methane than the fresh material. Acidic additives preserved and even increased the amount of the valuable water-soluble carbohydrates during storage, which affected most significantly the enzymatic hydrolysis yield of maize. However, preservation without additives decreased the enzymatic hydrolysis yield especially in maize, due to its high content of soluble sugars that were already converted to acids during storage. Urea-based preservation significantly increased the enzymatic hydrolysability of hemp. Hemp, preserved with urea, produced the highest carbohydrate increase of 46% in enzymatic hydrolysis as compared to the fresh material. Alkaline pretreatment conditions of hemp improved also the methane yields. Conclusions: The results of the present work show that ensiling and alkaline preservation of fresh crop materials are useful pretreatment methods for methane production. Improvements in enzymatic hydrolysis were also promising. While all three crops still require a more powerful pretreatment to release the maximum amount of carbohydrates, anaerobic preservation is clearly a suitable storage and pretreatment method prior to production of platform sugars from fresh crops. © 2011 Pakarinen et al; licensee BioMed Central Ltd. 
## 
## Topic 4: Biomass
## V11: 10.1016/j.biombioe.2021.106010
## This paper studies the effect of mechanical pretreatment of NPKCa-containing waste (vegetable waste VW and thickened sewage activated sludge TSAS) on biogas production and agronomic quality of the digestates. The efficiency of this process was evaluated by the yield of methane during anaerobic co-digestion with bioadditive (cheese whey), as well as the nutrients content in digestates by using an agrochemical test. It was found that with increasing comminution intensity the particle size of the substrate decreases from 50 to 16 μm which causes an increase in biogas yield by ~30%. As a result, the carbon content in the digestates decreases from 72 to 44%, whereas nutrients content increases by, %TS: N – 3.4; P – 3.7; K – 3.7; Ca – 7.6. The grinded substrate was used for anaerobic digestion with the addition of various amounts of cheese whey. Obtained digestates have a stimulating effect on the growth of barley and peas plants. In particular, digestion with 3 wt% of whey had the maximum impact on plant yield (+0.3–0.4 t ha−1 compared to control). Moreover, the combination of vigorous comminution with the addition of whey leads to a significant reduction in the fermentation duration (up to 4 times) and increases biogas production by 41%. Obtained digestates can be considered as organo-mineral fertilizers since they contain up to 43% of the total amount of NPKCa and meet the requirements for the content of heavy metals under Ukrainian legislation. 
## 
## Topic 4: Biomass
## V12: 10.1016/j.jclepro.2014.06.083
## Composting is considered an economically-friendly procedure for producing commercial solid organic amendments and fertilisers from the two-phase olive mill waste (called &quot;alperujo&quot;: AL), main by-product of the Spanish olive oil industry. AL composts are characterized by a noticeable organic matter content, mainly of lignocellulosic nature, which determines their humic properties. In this study, we have assayed several extraction conditions in order to release commercial liquid organic fertilisers from AL composts. The following conditions were tested: extraction time, extraction temperature, heat (70 °C) time applied, extraction ratio, extractant agent and alkali concentration. Their effects on organic fraction (total organic, polyphenol- and carbohydrate-like carbons), nutrient concentration and extraction efficiency were evaluated. In general terms, an increase in the extraction time and the combined use of alkali and heat increased significantly the amount of organic carbon solubilised from the compost, affecting the nature of the alkali-soluble organic matter and even showing a chemical degradation of the humic fraction in some cases. The extraction ratio modified the concentration of the organic and inorganic fraction in the extracts, and also their polyphenol and carbohydrate content. The use of a 24 h extraction with 1 M KOH (1:4 or 1:5, extraction ratios) and heat (4 h at 70 °C) allowed us to extract the required amount of C and K from AL compost, being necessary an external source of N and P to complete the fertiliser formulations according to the current Spanish legislation for Organo-mineral Fertiliser production (RD 506/2013, 2013). © 2014 Elsevier Ltd. All rights reserved. 
## 
## Topic 4: Biomass
## V13: 10.1016/j.ijhydene.2012.07.095
## The formate-hydrogen lyase (FHL) complex of Escherichia coli catalyzes the conversion of formate to hydrogen (H2) and carbon dioxide (CO 2) under anaerobic conditions in the absence of exogenous electron acceptors. The FHL complex consists of formate dehydrogenase (FdhH) and hydrogenase 3 (Hyd3) that are involved in a series of reactions, including formate oxidation (by FdhH), electron transport (by putative five small subunits of Hyd3) and proton reduction (by HycE, large subunit of Hyd3). In this study, the FHL gene cluster and iscR, a negative regulator of iron-sulfur cluster, of E. coli SH5 (BW25113 ΔhycA ΔhyaAB ΔhybBC ΔldhA ΔfrdAB) were altered to increase the FHL-dependent H2 production activity. When FhlA, a transcriptional regulator of FHL, was overexpressed, the FHL-dependent H2 production activity was improved significantly to 2.03 μmol H2 mg-1 cdw min-1 from 1.41 μmol H2 mg-1 cdw min-1. When an iscR deletion and/or fdhF overexpression was added, the whole-cell FHL activity was improved further to 2.80 μmol H2 mg-1 cdw min -1 (with ΔiscR) and 2.45 μmol H2 mg-1 cdw min-1 (with ΔiscR plus fdhF overexpression), respectively. The increase in whole-cell FHL activity was accompanied by an increase in the activity of its member enzymes, such as HycE and/or FdhH. In addition, the highly active recombinant strains exhibited stable performance during prolonged H2 production with the repeated addition of formate. Overall, the FHL-dependent H2 production activity of E. coli can be improved more than three-fold by modifying the expression of the relevant genes. © 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved. 
## 
## Topic 4: Biomass
## V14: 10.1016/j.jclepro.2019.118308
## Fluidized bed pyrolysis (FBP) is a promising technique for disposal of textile dyeing sludge (DS) owing to its unique characteristics, economic and environmental benefits. However, only a limited number of relevant studies have been found. This work investigated pyrolysis characteristics of DS in a fluidized bed at different operation conditions. The results showed the non-condensable gas yield increased and FBP char (FC) yield gradually decreased with increasing temperature. The maximum oil yield (i.e. 3.52 wt%) was achieved at 650 °C. Higher temperatures promoted release of heavy metals and FCs retained heavy metals with residue ratios (RRs) ranging from 64 to 89 wt% at 450–850 °C. CaO, kaolin and Ca-bentonite contributed to enrichment of Pb, Zn, Ni and Cr in FCs at various degrees. Ca-bentonite achieved the highest RRs of 98.08% for Ni, 97.96% for Zn and 96.08% for Pb. K3NiO2, ZnAl2O4, ZnS, PbSiO3 and Pb3SO6 were detected in FCs, while only ZnAl2O4 was found in DS. The extraction results by BCR (Community Bureau of Reference) four-step methods displayed that higher temperature (e.g. 650 °C) and addition of catalysts (e.g. Ca-bentonite) converted heavy metals (e.g. Cr and Ni) from oxidizable fraction to residual fraction, thus realizing less environmental toxicity. All heavy metals contents in FCs conformed to national standard. 
## 
## Topic 4: Biomass
## V15: 10.1186/s13068-018-1209-6
## Background: During the process of bioethanol production, cellulose is hydrolyzed into its monomeric soluble units. For efficient hydrolysis, a chemical and/or mechanical pretreatment step is required. Such pretreatment is designed to increase enzymatic digestibility of the cellulose chains inter alia by de-crystallization of the cellulose chains and by removing barriers, such as lignin from the plant cell wall. Biological pretreatment, in which lignin is decomposed or modified by white-rot fungi, has also been considered. One disadvantage in biological pretreatment, however, is the consumption of the cellulose by the fungus. Thus, fungal species that attack lignin with only minimal cellulose loss are advantageous. The secretomes of white-rot fungi contain carbohydrate-active enzymes (CAZymes) including lignin-modifying enzymes. Thus, modification of secretome composition can alter the ratio of lignin/cellulose degradation. Results: Pleurotus ostreatus PC9 was genetically modified to either overexpress or eliminate (by gene replacement) the transcriptional regulator CRE1, known to act as a repressor in the process of carbon catabolite repression. The cre1-overexpressing transformant demonstrated lower secreted cellulolytic activity and slightly increased selectivity (based on the chemical composition of pretreated wheat straw), whereas the knockout transformant demonstrated increased cellulolytic activity and significantly reduced residual cellulose, thereby displaying lower selectivity. Pretreatment of wheat straw using the wild-type PC9 resulted in 2.8-fold higher yields of soluble sugar compared to untreated wheat straw. The overexpression transformant showed similar yields (2.6-fold), but the knockout transformant exhibited lower yields (1.2-fold) of soluble sugar. Based on proteomic secretome analysis, production of numerous CAZymes was affected by modification of the expression level of cre1. Conclusions: The gene cre1 functions as a regulator for expression of fungal CAZymes active against plant cell wall lignocelluloses, hence altering the substrate preference of the fungi tested. While the cre1 knockout resulted in a less efficient biological pretreatment, i.e., less saccharification of the treated biomass, the converse manipulation of cre1 (overexpression) failed to improve efficiency. Despite the inverse nature of the two genetic alterations, the expected &quot;mirror image&quot; (i.e., opposite regulatory response) was not observed, indicating that the secretion level of CAZymes, was not exclusively dependent on CRE1 activity. 
## 
## Topic 4: Biomass
## V16: 10.1186/s13068-016-0616-9
## Background: The filamentous fungus Penicillium oxalicum is a potential alternative to Trichoderma reesei for industrial production of a complete cellulolytic enzyme system for a bio-refinery. Comparative omics approaches can support rational genetic engineering and/or breeding of filamentous fungi with improved cellulase production capacity. In this study, comparative genomic, transcriptomic and secretomic profiling of P. oxalicum HP7-1 and its cellulase and xylanase hyper-producing mutant EU2106 were employed to screen for novel regulators of cellulase and xylanase gene expression. Results: The 30.62 Mb P. oxalicum HP7-1 genome was sequenced, and 9834 protein-coding genes were annotated. Re-sequencing of the mutant EU2106 genome identified 274 single nucleotide variations and 12 insertion/deletions. Comparative genomic, transcriptomic and secretomic profiling of HP7-1 and EU2106 revealed four candidate regulators of cellulase and xylanase gene expression. Deletion of these candidate genes and measurement of the enzymatic activity of the resultant mutants confirmed the identity of three regulatory genes. POX02484 and POX08522, encoding a putative Zn(II)2Cys6 DNA-binding domain and forkhead protein, respectively, were found to be novel, while PoxClrB is an ortholog of ClrB, a key transcriptional regulator of cellulolytic enzyme gene expression in filamentous fungi. ΔPOX02484 and ΔPOX08522 mutants exhibited significantly reduced β-glucosidase activity, increased carboxymethylcellulose cellulase and xylanase activities, and altered transcription level of cellulase and xylanase genes compared with the parent strain ΔPoxKu70, with Avicel as the sole carbon source. Conclusions: Two novel genes, POX02484 and POX08522, were found and characterized to regulate the expression of cellulase and xylanase genes in P. oxalicum. These findings are important for engineering filamentous fungi to improve cellulase and xylanase production. 
## 
## Topic 4: Biomass
## V17: 10.1039/c8se00408k
## In this investigation a novel synergistic approach for the bioremediation of metal-contaminated water and bioenergy production was developed. Two microalgae, Chlorella vulgaris and Arthrospira platensis (Spirulina), and two macroalgae, Ulva lactuca and Sargassum muticum, were used as passive bioremediation agents for the metals Ni(ii), Zn(ii), Cd(ii) and Cu(ii). The metals were added singularly and in combination between 10-150 mM. The metal contaminated biomass was then processed through hydrothermal liquefaction to yield four phases: a bio-crude oil, an aqueous phase, solid residue and gas. Both C. vulgaris and A. platensis gave high bio-crude yields of 39 and 31 wt% respectively, while U. lactuca and S. muticum gave 14% and 9% respectively. Initial studies demonstrated that the addition of up to 150 mM of the target metal sulfates to the biomass feedstock did not significantly affect bio-crude production, and, for microalgae, over 99% of the target metals were partitioned to the solid phase products predominantly as phosphates or oxides. Subsequently, bioremediation of waste water and HTL were successfully coupled, with over 80% of a 10 mM solution of the metals biosorbed, though efficacy depended heavily on the algal species. Upon HTL of the remediating biomass, the yield and composition of the bio-crude were not changed significantly. For the microalgae, the aqueous phase contained significant nitrogen, potassium and phosphate levels, and the majority of the target metals deposited in the solid phase, with over 99.5% metal recovery for Spirulina when all four metals were used. The macroalgal species were not as effective in this process, with limited phosphate recovery in the aqueous phase (albeit with extensive potassium recovery) and with less than 50% of the target metals depositing in the solid residue for the Ulva species examined, presumably due to the affinity of the metals to proteinous species rather than polysaccharide in this species. Combining microalgal bioremediation with hydrothermal liquefaction is therefore a potentially highly effective method of remediating contaminated waste waters, whilst a macroalgae based process may offer a cheaper alternative, albeit with substantially reduced efficacy. The recovery of the target metals and multiple product formation improves the economic viability of the process, thereby valorising the bioremediation process and subsidising environmental clean-up. 
## 
## Topic 4: Biomass
## V18: 10.1016/j.fuel.2022.125248
## Catalyzed Diesel Particulate Filter (CDPF) provides an efficient method for removing particulate matter emissions to meet stringent soot emission legislation for diesel engines. In order to clarify the temperature and pressure characteristics of CDPF during the soot loading and regeneration process, in this study, a self-defined transient cycle is used for the soot loading test and combined with 1D simulations to reveal the pressure field. In addition, the effect mechanism of initial soot loading and inlet parameters on temperature and soot characteristics was investigated through the results of regeneration experiments and 3D simulations. The results show that the initial soot loading amount and exhaust flow rate affected the pressure drop of CDPF more than that of the engine. The total pressure drop of the CDPF varied linearly with the increasing exhaust flow rate and soot loading. Among the four pressure drops, the soot layer pressure drop had the highest proportion and the most significant change. During the active regeneration of CDPF, the temperature near the CDPF outlet was the highest (TS3), and TS1 was the lowest, in the axial direction, TS4 and TS5 in the radial direction were the highest. In addition, the soot combustion process can be divided into stages I and II based on the soot oxidation speed. The larger initial soot loading, the higher inlet temperature, the faster exhaust flow rate, and greater O2 concentration all resulted in the shorter stage I, the larger soot oxidation speed, the faster temperature rise, the larger region of high temperature in CDPF. And the inlet temperature had the greatest impact on the regeneration process, followed by initial soot loading and O2 concentration, and the exhaust flow rate had the least impact. There is a reversed change in the propagation direction during the oxidation of soot. 
## 
## Topic 4: Biomass
## V19: 10.1016/j.jclepro.2017.07.232
## Microalgae can be used for wastewater bioremediation with simultaneous CO2 biofixation producing valuable biomass. Wastewater from a brewery was treated using the Scenedesmus obliquus microalga in bubble-column photobioreactors (PBRs). The PBRs were fed with ambient air and the effect of a 10% (v/v) brewery CO2 supplement was studied. The PBRs were inoculated and a range of mean hydraulic residence time (HRT) values were tested (2.1–10.4 days). The maximum ash-free dry weight (AFDW) biomass productivity was obtained for a HRT of 3.5 days (0.29 d−1 dilution rate; 0.2 g L−1 d−1 (in terms of AFDW). The highest pollutant removal efficiencies were 92.9 and 88.5% for ammonia and total nitrogen, respectively, 40.8% for phosphorus, and 61.9% for COD. Except a dilution rate of 0.48 d−1 trial, the treated wastewater always met the Portuguese legislation quality standard for discharge into natural water bodies. Aiming to simultaneously maximize biomass volumetric productivity, CO2 biofixation rate and wastewater treatment efficiency, while minimizing residence time, 0.29 d−1 represents the optimal dilution rate value. The potential of the produced Scenedesmus obliquus biomass was evaluated for the generation of biohydrogen through dark fermentation with Enterobacter aerogenes, and of bio-oil, bio-char and bio-gas through a pyrolysis process. The yields obtained were 67.1 mL H2 g−1 (in terms of volatile solids - VS) for bioH2 and 64, 30 and 6% for bio-oil, bio-char and bio-gas, respectively (dry mass content (%) calculated over freeze dryer biomass basis). 
## 
## Topic 4: Biomass
## V20: 10.1039/c7ta05389d
## Nanostructured metal sulfides hold great promise for adsorption and catalysis applications, but it remains challenging for them to achieve highly efficient decontamination and facile separation of heavy metal ions from water. Herein, Fe3S4/reduced graphene oxide composites (Fe3S4/rGO) were successfully prepared and utilized for the removal of Pb(ii) from water. Uniform Fe3O4 nanoparticles were firstly dispersed on the rGO, and employed as sacrificial materials for a facile sulfuration approach. This gave rise to the fabrication of Fe3S4 nanoparticles (Fe3S4 NPs) with an average size of 14.3 nm. The resultant Fe3S4/rGO presented an evidently greater adsorption capacity (285.71 mg g-1) and advantages in driving fast adsorption of Pb(ii) in comparison with Fe3O4/rGO (106.27 mg g-1). A remarkable selectivity for Pb(ii) was achieved in the presence of coexisting cations and anions, and the addition of humic acid promoted the removal of Pb(ii) by Fe3S4/rGO. The enhanced performance of Fe3S4/rGO was mostly facilitated by synergetic contributions of the strong and selective Pb-S interactions between Pb(ii) and small Fe3S4 NPs associated with the surface adsorption. Remarkably, a single treatment of smelting wastewater with Fe3S4/rGO effectively reduced Pb(ii) concentration below the drinking water standard that is recommended by the U.S. Environmental Protection Agency, along with a surprisingly high removal efficiency toward arsenic (96.18%). However, Fe3O4/rGO merely exhibited a low removal rate of 29.59% for Pb(ii). The efficient removal performance of Fe3S4/rGO exhibited its great potential in the remediation of heavy metal polluted water via simultaneously taking advantage of magnetic properties and a high affinity for heavy metals. 
## 
## Topic 4: Biomass
## V21: 10.1016/j.apenergy.2013.06.022
## The Chemical Looping with Oxygen Uncoupling (CLOU) process is a type of Chemical Looping Combustion (CLC) technology that allows the combustion of solid fuels with air, as with conventional combustion, through the use of oxygen carriers that release gaseous oxygen inside the fuel reactor. The aim of this work was to study the behaviour of the sulphur present in fuel during CLOU combustion. Experiments using lignite as fuel were carried out in a continuously operated 1.5kWth CLOU unit during more than 15h. Particles containing 60wt.% CuO on MgAl2O4, prepared by spray drying, were used as the oxygen carrier in the CLOU process. The temperature in the fuel reactor varied between 900 and 935°C. CO2 capture, combustion efficiency and the sulphur split between fuel and air reactor streams in the process were analysed. Complete combustion of the fuel to CO2 and H2O was found in all experiments. Most of the sulphur introduced with the fuel exited as SO2 at the fuel reactor outlet, although a small amount of SO2 was measured at the air reactor outlet. The SO2 concentration in the air reactor exit flow decreased as the temperature in the fuel reactor increased. A carbon capture efficiency of 97.6% was achieved at 935°C, with 87.9wt.% of the total sulphur exiting as SO2 in the fuel reactor. Both the reactivity and oxygen transport capacity of the oxygen carrier were unaffected during operation with a high sulphur content fuel, and agglomeration problems did not occur. Predictions were calculated regarding the use of a carbon separation system in the CLOU process in order to reduce sulphur emissions. Coals with high sulphur content, such as lignite and anthracite, would require a carbon separation system in order to comply with legislation governing sulphur-limits. In conclusion, coals with a high sulphur content can be burnt in a CLOU process using Cu-based material to obtain high carbon capture efficiencies. © 2013 Elsevier Ltd. 
## 
## Topic 4: Biomass
## V22: 10.1016/j.petrol.2011.12.010
## The success of biotechnological processes for oil recovery depends on adequate understanding of the system components: microorganisms, oil and porous media.Nine oil samples were collected from a carbonate oil reservoir in Cordoba Platform, Veracruz, Mexico. Geochemical characterisation demonstrated heavy oils with: API gravity of 10.7 to 14.5°, 3 to 5% sulphur, 36.58 to 54.06% aromatic hydrocarbons and -23.98 to -24.24 (‰) δ13C isotopic values in total oil. Additionally, the pristane/phytane ratio (Pr/Ph)&lt;1 indicated an anoxic depositional environment. Results showed that native microbiota could have contributed to the biogeochemical transformation of the oil in the reservoir. Anaerobic, thermophilic, halotolerant and fermentative enrichment cultures were obtained from the nine oil samples (A1-A9). Metabolites such as CO2, CH4, ethanol, acetone, acetate and biosurfactants were detected. Mixed culture from the A7 sample showed the highest activity among all the mixed cultures screened, growing under 50 to 80°C, 5 to 35gL-1 NaCl and 0.8 to 14.2MPa pressure. Its kinetic parameters were μmax=0.321h-1 and Ks=0.54gL-1. These results were used to determine the conditions for oil recovery assays. A7 culture enhanced recovery of up to 12% of heavy oil (11.16°API) in oil impregnated carbonate granular porous media. Analysis of the V3 region of the 16S rRNA gene of the A7 culture showed that the predominant taxon was Thermoanaerobacter. Phylogenetic approximations demonstrated that similitude percentage of 99.9% corresponded to Thermoanaerobacter ethanolicus, 99.6% to Thermoanaerobacter pseudethanolicus and 98.9% to Thermoanaerobacter brockii and Thermoanaerobacter finii. © 2011 Elsevier B.V. 
## 
## Topic 4: Biomass
## V23: 10.1016/j.jclepro.2019.117832
## We develop a novel approach to recover chromium and iron from hazardous vanadium extraction tailings containing high chromium concentrations by synergistic carbothermal reduction of the mixture of chromite and the vanadium extraction tailings. Experiments show that the recoveries of chromium and iron reach 97.81% and 96.82%, respectively, and decrease with increasing mass ratio of chromite and high chromium type vanadium extraction tailings. The recoveries of chromium and iron are higher when graphite powder is used as reductant. The contents of impurity elements C, Si, S and P fluctuate within a small range and satisfy the requirements of national standards. The addition of CaO can greatly reduce the sulfur content in the high carbon ferrochromium product. 98.34% Cr (VI) and 100% V in the tailings were converted during the reduction process. It is demonstrated that carbothermal reduction could efficiently detoxify Cr(VI) in vanadium extraction tailings containing high chromium concentrations. 
## 
## Topic 4: Biomass
## V24: 10.1016/j.jaap.2017.06.026
## Biomass of Flourensia oolepis, a native shrub of the semi-arid central region of Argentina, was treated by vacuum pyrolysis to investigate the potential of this species to be used as a source of energy and chemicals. In this study we determined the influence of temperature on the product yields in different plant organs (leaves and stems), characterized the bio-oil, and assessed the bioactivity of biochar aqueous extracts through germination and growth bioassays with Lactuca sativa. The pyrolysis oils showed a predominance of long chain, cyclic and aromatic hydrocarbons in the leaves pyrolysate. The sesquiterpene spathulenol was the major compound in these reactions. Pyrolysis of stems produced mainly phenolic compounds. The effect of phosphoric acid pretreatment on leaves and stems was also evaluated in order to improve bio-oil yield and selectivity to any interesting compound. The results showed that acid treatment enhanced the liquid formation in pyrolysis of leaves giving a high amount of long chain hydrocarbons compared with the untreated organ. The biochar water extracts from leaves exhibited a hormetic type of response, with a promoting growth effect on roots and shoots up to 225%, and only a transient inhibition of germination at higher doses (≥7.5% w/v). Biochar water extracts from stems did not affect seed germination and showed a remarkable promoting effect, stimulating growth at all concentrations tested up to 330%. Although additional testing is required, overall results show F. oolepis as a promissory species for the production of bio-oil and biochar with a wide range of applications, including the potential use as growth regulator. 
## 
## Topic 4: Biomass
## V25: 10.1016/j.jaap.2016.09.011
## The formation of the 16 polycyclic aromatic hydrocarbons (PAH), classified as priority pollutants by the Environmental Protection Agency (EPA), has been studied in the pyrolysis of dimethyl carbonate (DMC) at different reaction temperatures (1075–1475 K). PAH have been quantified by gas chromatography-mass spectrometry (GC–MS). The PAH speciation showed that C10-C16 PAH: NAPH, ACNY, PHEN, FANTH PYR, FLUO, ANTH, and some C20-C22 PAH: B[a]P, I[123-cd]P, and B[ghi]P, were present in high concentrations. The predominant PAH were NAPH and ACNY, and were found in significant concentrations in all phases analysed (gas phase, adsorbed on soot, and stuck on reactor walls). PAH were predominantly present in the gas phase, except at higher temperatures where most of the PAH were adsorbed on soot. The toxic potential, B[a]P-eq concentration, was determined according to the amount of PAH present in the different phases. The results showed that the highest B[a]P-eq concentration was found for the PAH adsorbed on soot corresponding to the experiment performed at 1375 K. 
## 
## Topic 5: Sustainability
## V1: 10.1016/j.spc.2020.02.006
## Making an effective sustainability decision at every stage of a product life cycle is key to achieving a holistic sustainable product development. The extant literature highlights the challenges and lack of effective tools for determining the impact of manufacturing processes on the environmental, economic and social dimensions, as well as the interdependence of the outcome of one dimension on the other. This research paper identifies methodologies, tools, and approaches that can be integrated into a single descriptive framework to enable both assessment and analysis of the aspects of the three sustainability dimensions. The paper also details the development of the framework using inductive methods and conceptual synthesis of key sustainability approaches and a Delphi study involving panels of international researchers and practitioners in the field of sustainable manufacturing. The framework can provide a platform for both practitioners and sustainability analysts to build impact analysis models that will support effective sustainability decision-making. It will also enable a clear perspective of the required elements, processes and indicators that need to be considered in sustainable manufacturing design and assessments. 
## 
## Topic 5: Sustainability
## V2: 10.1016/j.jclepro.2019.05.055
## A sustainable construction ought to enhance the quality of social, economic and environmental practices by determining the current sustainability level and identifying the weak points and consequently improving them. Even though so many studies have been carried out the context of economic and environments in construction projects, less attention was always paid to the social aspect of the construction projects. In this article, a novel construction social sustainability performance evaluation is presented based on fuzzy logic to evaluate the current social sustainability status in associated construction. This study originally adds to the state-of-the-art literature on sustainable construction by articulating a proposal and applications of a new as fuzzy construction social sustainability index. Critical social sustainability attributes are collected from the literature review and expert&#39;s judgments to address main dimensions and enablers of construction projects. The proposed index is validated by both triangular fuzzy and crisp approaches, and the results showed that all approaches lead to the same conclusions. Fuzzy performance importance index is then obtained and ranked for all attributes to identify obstacles and challenges behind the social sustainable construction concept. The scientific value of the proposed model is determined by its proper contribution in computing the new index with trapezoidal fuzzy principles and identifying the obstacles of associated construction. 
## 
## Topic 5: Sustainability
## V3: 10.1016/j.jclepro.2019.03.307
## Risk on the supply chain from various sustainability-related factors has increasingly become relevant to companies in many industries. There however still lacks a comprehensive method to quantitatively evaluate supply chain sustainability risk taking into account the triple bottom line of sustainability (economic, social, and environmental). This study develops a framework to evaluate supply chain sustainability risk by measuring supply chain-wide operational risk, social risk, and environmental risk to form an aggregate metric. A set of indicators readily available in literature are used to represent various aspects of supply chain sustainability risk. Risk assessment space and materiality analysis are then used to prioritize resource allocation among supply chain stages from two distinct perspectives for mitigating supply chain sustainability risk. Two case studies are provided to demonstrate the application of the developed framework, representing two main types of supply chain structure. The apparel industry example represents the deep-structure supply chain driven by relatively simple products, while the automotive industry example represents the broad-structure supply chain driven by more complex products. While the case studies focus on industry-level assessment for demonstration purpose, the developed framework can be flexibly applied to evaluate supply chain sustainability risk for specific companies in any industry. 
## 
## Topic 5: Sustainability
## V4: 10.1016/j.jclepro.2012.01.022
## This contribution presents an integrated quantitative approach for the holistic assessment of the sustainability of technologies. The methodology envisages environmental, economic and social sustainability assessments separately, then collates them to obtain a single measure of sustainability. Standard life cycle assessment and economic evaluation methods are used to provide quantitative measures for environmental and economic parameters. A new method is used to evaluate the social sustainability, enabling the quantitative integration of all three indices. Individual indices are developed and combined to provide a single performance index of sustainability. This particular tool allows technology stakeholders to incorporate sustainability principles to their design and operation activities. It provides also a benchmark platform for comparing various technologies from a sustainability standpoint. © 2014 Elsevier Ltd. 
## 
## Topic 5: Sustainability
## V5: 10.1016/j.jclepro.2015.08.105
## As the growth in demand for sustainable manufacturing continues, companies must begin to make conscious decisions with regard to the sustainability of their products. Thus, design and manufacturing engineers must consider economic, environmental, and social aspects simultaneously when developing products and process flows. The purpose of this research is to develop a sustainable assessment methodology to both improve the accuracy of existing approaches in identifying the sustainability impacts of a product and to assist manufacturing decision makers. The methodology developed utilizes unit process modeling and life cycle inventory techniques. Combining these approaches allows for conducting product sustainability assessment at the process level by quantifying a selected set of sustainability metrics. A demonstration of the methodology to assess three design alternatives for a bevel gear is presented. The developed methodology is capable of quantifying the sustainability metrics by aggregating information from the process level. It was found that the various metrics require different aggregation methods from the manufacturing process to the manufacturing system level. The general approach can be applied to aid the investigation of tradeoffs during the design decision making process for a wide range of products. 
## 
## Topic 5: Sustainability
## V6: 10.1016/j.jclepro.2021.129167
## Highway engineers should ideally select sustainable road designs based on accurate and comprehensive assessments. Nevertheless, it is often challenging to identify the significant factors for quantifying the multidimensional concept of sustainability. Moreover, due to the multidisciplinary nature of sustainability, the outputs from various assessments are often diverse, and, hence, their significance must be suitably examined during the decision-making process. In the present study, economic, environmental, and social life cycle assessments (LCAs) were considered in an integrated manner, and 12 measurable indicators were thereby recommended for assessing specific road designs. Since the LCA outputs are usually sensitive to the quality of the inputs used for the calculation, customizable LCA models and project-specific data are required to produce a more precise estimate than that based on the standard available information. In this study, a hybrid analytic hierarchical multi-criteria group decision-making model was employed to select the sustainable design. In addition, the application of the proposed approach was demonstrated by selecting a sustainable road design for El Paso, Texas. The environmental outputs were normalized using a relatable reference system to facilitate understanding. The assessments were then reviewed by eight experts, and a group-agreeable design was selected. 
## 
## Topic 5: Sustainability
## V7: 10.1016/j.jclepro.2017.08.154
## Selecting the most suitable supplier regarding to the environmental competencies is one of the main issues for companies and organizations. To address the issue, the group decision-making methodologies are well known approaches which could help decision makers (DMs) for evaluating the green supplier selection problems. In this study, a new model based on group decision-making approach is introduced under novel compromise ranking method and interval type-2 fuzzy sets (IT2FSs) for the green supplier selection problems. In the proposed approach, the assessment criteria are provided by DMs based on linguistic terms which are converted to interval type-2 fuzzy elements. In this respect, decision makers&#39; weights are computed based on an extended interval type-2 fuzzy TOPSIS method considering the priority experts&#39; opinions about the relative significance of criteria, and also a new ranking index is presented respecting to interval type-2 fuzzy Hamming distance measure to ranking potential alternatives. Hence, a real case study is considered to indicate the applicability of the proposed approach. In addition, results from the proposed approach are compared with the existing method in literature in order to illustrate the validation of proposed model. Furthermore, a sensitivity analysis is prepared to identify and determine the effects of different DMs&#39; weights on ranking results. Results show that the proposed approach is an effective and practical decision tool for green supplier selection problems in fuzzy environment. 
## 
## Topic 5: Sustainability
## V8: 10.1016/j.jclepro.2019.118640
## Achieving sustainability in product life cycle involves the complex decision making process such as the selection of conceptual schemes and manufacturing processes, and disposing method of end-of-life. Morphological matrix is simple and useful tool for mapping design requirements in conceptual design for new product design and redesign of out-of-date product. Although conceptual design using morphological matrix as an important step in the product development could achieve sustainability requirements through accurate choice, quantitative decision making method is seldom considered in selection of function solution principle for sustainability. Meanwhile, uncertainties of sustainability attributes in conceptual stage generally are not also considered in these morphological matrix. To solve these problems, this paper put forward an approach based on morphological matrix to satisfy sustainability requirements in product conceptual stage considering vagueness and uncertainty through intuitionistic fuzzy number. The aim of this paper is to quantify conceptual schemes considering sustainability attributes using group decision making and intuitionistic fuzzy preference relations. The decision makers’ weights are calculated based on theirs experiences and knowledge on the function, manufacturing, economic, environment, recycle and social properties of product. The morphological matrix is used to establish the relationship between function and solution principles based on sustainability information, then the preference degree are obtained from multiple experts employing intuitionistic fuzzy judgment matrix. The preference degree for each solution principle is quantified by intuitionistic fuzzy numbers through membership, non-membership and hesitancy degree to express the linguistic terminology. To ensure the consistency and consensus issues of preference relations, an additive consistency method is employed, and correction factor is used for achieving modification automatically of judgment matrix when the consistency requirement is not met. Finally, the integrated multi-criteria decision model for function solution principle is established to carry out the final sustainability decision problem in early design stage. The feasibility and validity of proposed method is proved through a practical example for spindle system design of CNC. 
## 
## Topic 5: Sustainability
## V9: 10.1016/j.jclepro.2016.09.195
## In the early phases of product development, it is important to both reduce costs and improve a product&#39;s sustainability performance. Insufficient data are available on the costs and sustainability aspects of innovative concepts such as automotive lightweight design, which require the application of new materials and processes. The lack of information and high degree of uncertainty hinder the use of traditional sustainability evaluation tools such as Life Cycle Assessment during these early phases. Tools used in eco-design and sustainable design have disadvantages since they either focus on only one sustainability dimension, require quantitative data about materials and processes, or cannot be applied by designers and engineers. To overcome these disadvantages, a new checklist for Sustainable Product Development (CSPD) was developed in close collaboration with practitioners. The CSPD allows the qualitative assessment of environmental, economic and social aspects during the early phases of product development while considering a full life cycle perspective. It provides the methodological foundation for an iterative process, in which improvement tasks related to sustainability are defined that must be completed by the engineers. The applicability of the CSPD with reference to a wide range of technologies was evaluated and tested in a case study that included nine automotive lightweight technologies. This case study revealed that the developed tool helped designers and engineers assess and improve the sustainability performance of a technology and that it stimulated processes of collaboration and information exchange within and between organizations. 
## 
## Topic 5: Sustainability
## V10: 10.1016/j.jclepro.2020.121802
## The survival of the manufacturing organizations has been challenged by global competition, dynamic customer requirements, and limited natural resources. Manufacturing organizations need to adopt innovative strategies to overcome such challenges. Lean Six Sigma is one of the key business strategies which has been employed by manufacturing organizations to improve their operational performance. The integration of Sustainability with Lean Six Sigma has created interest among the researchers and practitioners to improve sustainable performance. For any manufacturing organization, it is critical to identify the enablers for the successful Sustainable Lean Six Sigma implementation. The purpose of the present study is to identify and evaluate the enablers of Sustainable Lean Six Sigma. The enablers of Sustainable Lean Six Sigma in Indian manufacturing organizations have not been explored in past studies, which motivates the present case study. The enablers for Sustainable Lean Six Sigma implementation were identified through the combined approach of literature review and experts’ opinions. Fuzzy decision-making trial and evaluation laboratory technique was applied to evaluate the enablers for Sustainable Lean Six Sigma implementation in an electric component manufacturing organization. The relationships among the prominent cause enablers and effect enablers are established through the causal diagram. The top management commitment and involvement was found as the most significant enabler, followed by organizational readiness to implement Sustainable Lean Six Sigma, environmental management system. The present study is one of the initial studies to evaluate integrated (Sustainability and Lean Six Sigma) enablers for Sustainable Lean Six Sigma implementation. The results of the present study will assist the researchers and practitioners in implementing Sustainable Lean Six Sigma in manufacturing organizations by understanding the mutual relationships among the enablers. 
## 
## Topic 5: Sustainability
## V11: 10.1016/j.jclepro.2018.09.144
## This study proposes a supplier sustainability performance evaluation framework for evaluating and selecting suppliers based on their sustainability performance. An integrated model which uses fuzzy-Shannon Entropy to determine the sustainability criteria weights and fuzzy-Inference system to prioritize suppliers from the individual sustainability dimensions perspective is proposed to aid in the evaluation and selection. A Pakistan manufacturing company is used to exemplify the applicability and usefulness of the proposed suppliers’ sustainability performance evaluation decision framework. The results show that amongst the economic, environmental and social sustainability dimensions, three criteria, namely: ‘Quality’ (10.87%), ‘Cleaner Technology Implementation’ (11.51%) and ‘Information Disclosure’ (13.75%), respectively, are the topmost ranked criteria. Across the triple-sustainability dimensions, suppliers 3 was ranked the topmost suppliers overall. This means that, to improve the sustainability of the company&#39;s supply chain, supplier 3 is most appropriate and recommended amongst the four suppliers for partnership. Managerial implications, limitations and further research directions are discussed. 
## 
## Topic 5: Sustainability
## V12: 10.1016/j.jclepro.2018.09.235
## The main objective of this paper is to propose a method for the evaluation and selection of green suppliers for the Brazilian furniture industry. In order to achieve this goal, this study identifies environmental criteria for use in assessing and selecting green suppliers of the furniture industry; then, the study directs readers towards a successful implementation of GSCM practices. The proposed methodology uses a hybrid Entropy-TOPSIS-F framework to weight the criteria and select the supplier with the best environmental performance. The fuzzy approach is integrated with Shannon&#39;s Entropy and the TOPSIS method to deal with uncertainty in the decision-making process. To show the applicability and results of the proposed framework, we collected 32 experts’ information from the furniture industry about the environmental criteria that are more suitable for evaluation and selection of suppliers in this sector. A real case involving the evaluation, based on the defined criteria, of three potential suppliers by three managers of a furniture industry was discussed. According to the findings, “Management Commitment to GSCM” “Ecodesign” and “Environmental management system” are the first three criteria in the ranking of selection of sustainable suppliers. In addition, of the three potential suppliers of the case, supplier A1 presented superior environmental performance. A sensitivity analysis is performed to test the stability of the Entropy-TOPSIS-F approach and to verify the robustness of the proposed framework. 
## 
## Topic 5: Sustainability
## V13: 10.1016/j.jclepro.2019.01.220
## Despite progress in many areas, the concrete industry still confronts challenges when it comes to the topic of concrete sustainability. Several evaluation methods have been proposed in response to these challenges, but the lack of a holistic framework for sustainable concrete material remains. In this paper, a holistic indicator framework for concrete material sustainability evaluation is developed by aggregating and analyzing the multitude of sustainable concrete material indicators into a causal network that reveals the indicators&#39; interrelationships. A novel component of this framework is the integration of the two global perspectives on sustainable development: the pillars of sustainability (environment, economy, society) and the Sustainable Development Goals (SDGs). These sustainability perspectives provide the framework with clear context to underpin the concept of sustainable concrete. A demonstration study validated the framework&#39;s applicability in evaluating the sustainability of various concrete mixes. The results highlight the importance of integrating both sustainability perspectives in elucidating the trade-offs that exist between the facets of sustainability and the priority areas needing attention to enhance concrete sustainability. These are critical information in the need to balance the different facets of sustainability, and therefore support the decision-making strategies for concrete sustainability. 
## 
## Topic 5: Sustainability
## V14: 10.1016/j.spc.2015.08.006
## The continued existence of life on the planet depends on the quality of the environment, which is responsible for providing humanity&#39;s basic resources. The concept of sustainability was thought up in order to create conditions for protecting the environment. It is founded on three main aspects, namely, social, economic, and environmental. Thus, the aim of this study is to identify and select a set of indicators in order to measure the industrial sustainability of the micro and small-sized furniture industry in particular. This study follows the qualitative/quantitative exploratory method, so the text mining technique was used in the literature review, and the Delphi method was used in the application of the survey, and in its analysis, the level of consensus and weighted average. The results present a set of twelve environmental indicators, seven social and seven economic, with a cut-off criterion for the environmental and social indicators in level of consensus ≥ 0.7 and weighted average ≥ 4.5, and for the economic indicators in LC ≥ 0.5 and WA ≥ 4.2. The selected indicators are in line with the desired qualities and necessary characteristics to make these industries more sustainable. It is concluded that the proposed set of indicators entails a holistic view which covers the triple bottom line, selected based on the criterion for quick measurement of sustainability and its specific application in micro and small furniture industries. 
## 
## Topic 5: Sustainability
## V15: 10.1016/j.jclepro.2023.136837
## For the last decades, Sustainable Development has been encouraged to be integrated into the operations of business organisations. Businesses are being blamed to cause environmental degradation and social problems because of their unsustainable activities. However, to know whether businesses are implementing sustainable strategies in their operations, a Business Sustainability Performance Assessment (BSPA) is required. As a result, many approaches, tools, and techniques have been devised to assess sustainability performance at the business level. Therefore, this study aims at identifying the various sustainability assessment approaches, tools, and techniques at the business level. A systematic literature review was conducted whereby 38 related articles from 2000 to 2021 were obtained and analyzed. Following the review, 5 different approaches were identified namely: application of established guidelines; normative framework; management system; index system; and rating/ranking system. Under each approach, there are different tools. In all, 88 tools were obtained. Furthermore, 9 techniques were identified and classified into 4 functional categories. Techniques such as Fuzzy Logic, Multi-Criteria Decision Analysis, and Principle Component Analysis were predominantly favoured by researchers. However, there was no consensus on which method or tool is best for BSPA. A bibliometric analysis found that 60% of studies used the environmental, social, and economic dimensions for performance assessment. Results demonstrated that the application of BSPA approaches, tools, and techniques had been receiving attention across various industries and the application was more toward industry-generic context rather than industry-specific. Finally, the paper presents key research issues on BSPA and proposes future studies based on reported data. 
## 
## Topic 5: Sustainability
## V16: 10.1016/j.jclepro.2021.130311
## In the decision-making process of the best available techniques (BAT), there are important uncertainties such as the absence of versatile defined evaluation criteria and systematic decision-making models. This study aimed to contribute to the development and systematization of decision-making processes for BATs in industrial facilities. An exemplary application was carried out in an integrated home textile production facility. A comprehensive cleaner production analysis study was carried out in the facility and an initial BAT list (total of 52 BATs) was prepared. In addition, a total of 40 novel evaluation criteria was defined in seven main groups such as resource use, environmental benefits, side interactions, economic saving, and applicability. A total of 15 different multi-criteria decision-making models were investigated and among them the preference ranking organization method for enrichment evaluations (PROMETHEE), technique for order preference by similarity to ideal solution (TOPSIS), multi criteria optimization and compromise solution technique (VIKOR), additive ratio assessment (ARAS), and complex proportional assessment (COPRAS) decision-making models were selected for use in the BAT decision-making process. Employing these models, most appropriate BATs were selected for the facility. In addition, the results of the studied 5 models were technically compared. It was found that VIKOR and COPRAS models can be preferred in the process of deciding on BATs in cleaner production applications. Moreover, it was concluded that the defined evaluation criteria in this work were also sufficient for decision-making processes. It is believed that this paper will be a good technical guide and provide a significant contribution to BAT selection/decision-making processes in full-scale industrial cleaner production applications in terms of elimination of uncertainties and improvement of the overall decision-making process. 
## 
## Topic 5: Sustainability
## V17: 10.1002/sd.1976
## Supply chain requires simultaneous enhancement of the economic, environmental, and social performance of the business toward sustainability. For this aim, a sustainable performance assessment system is needed to be implemented for evaluating different supply chain segments and clarify supply chains&#39; indicators. The assessment can be performed based on three sustainability dimensions: economical, environmental, and social. The first purpose of this paper helps to identify sustainability issues and highlights the gaps and inconsistencies of literature. The second purpose of the study is to identify the most important indicators of sustainability for sustainable development supply chain of Iranian Oil Company stakeholder engagement. The indicators of the sustainable supply chain are studied and collected using metasynthesis approach, and the most important indicators of sustainability were identified for the company with Delphi technique. The proposed sustainability indicators can be useful for the researchers to develop practical and comprehensive measures in their respective industries. 
## 
## Topic 5: Sustainability
## V18: 10.1016/j.jclepro.2020.120771
## This paper proposes an empirical study aimed at characterizing the evolution of a company towards a sustainable manufacturing strategy, with a special emphasis on the role played by the business functions within the industrial organization. In the study, a methodology to evaluate and rank the sustainable manufacturing strategy in different production contexts is developed, stemming from the fact that there is a lack of objective methods for sustainable manufacturing strategy ranking in the literature. The analysis method consists of an Analytic Hierarchy Process applied to competitive priorities and manufacturing performances. It enables a structured reflection that considers the multiple perspectives of different decision-makers in business functions relevant to the implementation of sustainability in the manufacturing strategy. The main objective of the approach proposed in this study is to provide a methodology able to give an integrated view of the economic, environmental and social dimensions of the manufacturing plants. The proposed methodology is applied in two application case studies referring to two different production contexts. The application cases show the usefulness of the methodology to assess how sustainability is supported in the manufacturing strategy, with specific concern to the evolution of a manufacturing plant towards a sustainable manufacturing strategy. 
## 
## Topic 5: Sustainability
## V19: 10.1016/j.jclepro.2015.04.053
## Life cycle sustainability assessment has been claimed to be one of the most common methods for assessing sustainability of products and processes. It consists of the three methods life cycle assessment, life cycle costing and social life cycle assessment. However, the life cycle sustainability assessment framework is still under development and its application is still limited. This is substantiated not only by the lack of data and experience, but also by the proliferation of indicators provided by different institutions. Although indicators are available for the three sustainability dimensions, guidance for the indicator selection is missing. The bottleneck is not the lack of good indicators, but rather the lack of a clear indicator selection process. This appears to be one of the most crucial aspects as data availability, method development and interpretation of results heavily depend on this issue. Another obstacle for the practical implementation of life cycle sustainability assessment arises with the relatively high entrance level. Whereas, for the environmental dimension sufficient data and simplified methods are usually available, e.g. carbon footprint, the social and economic dimension are lacking of similar simplifications. Within this study a Tiered approach has been developed providing an indicator hierarchy and proposing a stepwise implementation concept. An indicator review has been performed according to the three criteria practicality, relevance and method robustness. Afterwards the indicators have been ranked in three tiers. The first tier (&#39;sustainability footprint&#39;) focuses on indicators, which are characterized as easily applicable indicators and as relevant for production processes and on global scales. The second tier reflects current best practice indicators already used in case studies and preferred by institutions. The last tier aims at giving a comprehensive set of sustainability indicators, even if this level may not be applicable immediately. The Tiered approach may not solve all challenges within life cycle sustainability assessment, e.g. The question of how to solve the interpretation dilemma still remains; however it does support the practical application and further development of the framework through the stepwise implementation of sustainability indicators. The application and science related benefits of the Tiered approach result from the undergone comprehensive indicator review, which seems essential as a basis for further developments within the life cycle sustainability assessment framework, and from the proposed indicator hierarchy, which provides directions for further research. The created sustainability footprint facilitates the practical implementation of life cycle sustainability assessment, as the entrance barrier was lowered without neglecting any dimension of sustainability. 
## 
## Topic 5: Sustainability
## V20: 10.1016/j.jclepro.2020.125021
## Both an environmental and an economic assessment are needed to judge the potential of sustainable chemical technologies. However, decision-makers may be challenged by conflicting conclusions. The integration of life cycle assessment (LCA) and techno-economic assessment (TEA) can enhance decision-making, as integrated assessments provide more information than a simple reporting of separate TEA and LCA results. The analysis of integration approaches reveals a lack of consistency in terms of defining criteria and methodological aspects for integration. A gap remains where guidance for practitioners is needed on how to select a suitable integration type for their different purposes. To fill this gap, we conclude that a one-size-fits-all solution of integration cannot adequately serve all purposes along the technology development phases. Therefore, a framework to guide through integration in three distinct parts is proposed. In Part I, a four-phase approach for every integrated assessment to link the results from TEA and LCA is defined. Part II develops three integration types classified by their core characteristics: qualitative discussion-based (Type A), quantitative combined indicator-based (Type B), and quantitative preference-based (Type C). Finally, in Part III, a step-by-step method to select the appropriate integration type according to the assessment purpose, while considering restrictions imposed by technology maturity and resource availability is introduced. Thus, the framework is a basis for increasing the number of integrated assessments by guiding practitioners towards tailored studies. 
## 
## Topic 5: Sustainability
## V21: 10.1016/j.jclepro.2020.125130
## This paper aims to construct a sustainability evaluation framework based on the extended VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method under a picture fuzzy environment. First, a sustainability evaluation index system for water environment treatment public-private-partnership (WET-PPP) projects is constructed from the perspectives of the economy, engineering, environment, management, and society, including five criteria and 20 sub-criteria. The constructed index system considers engineering sustainability and management sustainability, and can thus more comprehensively evaluate the “sustainability” of the project and meet the requirements of the new economic environment. Second, a corresponding method for the sustainability evaluation of WET-PPP projects is proposed based on the picture fuzzy similarity-based VIKOR method. The main advantages of the proposed method include the following: (1) the sustainability evaluation process for WET-PPP projects involves linguistic terms and picture fuzzy numbers (PFNs), which can not only more comprehensively describe the uncertainty of human cognition and decision-making processes in the form of positive, neutral, and negative membership functions, but can also reduce the difficulty for decision-makers (DMs) to provide evaluation information; (2) the proposed evaluation method combines picture fuzzy similarity and the traditional VIKOR method, and can therefore not only obtain compromise solutions but can also ensure the stability and feasibility of the evaluation results. Finally, a case study of WET-PPP projects in China is provided to testify the feasibility and effectiveness of the constructed framework. The results indicate that the proposed framework can improve the accuracy of decision-making and can be applied for handling evaluation problems. 
## 
## Topic 5: Sustainability
## V22: 10.1002/sd.1946
## In order to facilitate the shift towards sustainability in the building industry, development of building-specific sustainability assessment tools that evaluate and integrate related environmental and socioeconomic impacts is needed. Existing assessment tools mainly focus on environmental and economic issues and give limited consideration on social sustainability. This study fills this gap through proposing an integrated building-specific sustainability assessment model. This model was developed based on a conceptual model of life cycle sustainability assessment (LCSA), which is theoretically a combination of environmental life cycle assessment, life cycle cost analysis, and social life cycle assessment. An integrated AHP-ELECTRE approach was adopted to provide a logical backbone for the model. This LCSA model is demonstrated through a case study to choose the best alternative regarding sustainability from three structural designs, that is, modular construction-based, semiprefabricated, and conventional designs. Results show that semiprefabricated design is the best option in the context of this study. 
## 
## Topic 5: Sustainability
## V23: 10.1016/j.seta.2020.100719
## Rapid development in energy systems results in a choice question for decision-makers. Since many conflicting criteria are involved in the decision process, multi-criteria decision-making (MCDM) could be used. To make the decision process more suitable and reliable in reality, this paper introduces a sustainability prioritization framework with hybrid-data consideration via the integration of fuzzy group Delphi (FGD), fuzzy best-worst method (FBWM), and fuzzy best-worst projection (FBWP). In which, the triangular fuzzy number is incorporated into the framework to address the hybrid-data issues under uncertainty; the FGD rationally identifies the key criteria by implementing group consensus decisions, the FBWM accurately assigns the weights to the criteria by preserving the consistency in cognitive processes under uncertainty, and the FBWP rigorously ranks the alternatives by incorporating the absolute and relative performances regarding the multi-criteria. An illustrative case regarding ammonia production systems was studied, while the effectiveness and advantages of the framework were validated by results comparisons. In summary, this work makes methodological contributions to the sustainability prioritization of energy systems, including properly considering the uncertain hybrid data in decision-making, rationally selecting the key criteria and accurately determining their weights, and reliably ranking the alternative systems in the context of sustainability. 
## 
## Topic 5: Sustainability
## V24: 10.1016/j.jclepro.2017.03.126
## Forced by competitive, legislative and customers&#39; pressures, corporate executives are supposed to consider sustainability aspects of value creation which induces a new set of challenges within decision-making. Unfortunately, guidelines supporting comprehensive analysis, especially concerning the evaluation of environmental and social performance of decision alternatives, are missing. This hinders the advancements in corporate sustainability. Questions arise on how to measure and balance respective indicators with traditional economic objectives. To fill this void, this research sets out to develop an intuitive and holistic planning framework for advancing sustainability along supply chains. In a first step, we identify sustainability issues and indicators as well as decision-support methods by scrutinizing literature on sustainable supply chain management. Based on this, we develop a planning framework that supports executives in evaluating and deciding upon corporate sustainability initiatives. The framework comprises three consecutive phases of planning: ‘Problem definition’, ‘Assessment of alternatives’, and ‘Decision analysis’. For each phase we propose appropriate tools distinguishing concepts of sustainable supply chain management (e.g. Sustainable Logistics and Sustainable Manufacturing), methods for measuring the ecological and social efficiency of decision alternatives, and quantitative decision-support techniques able to balance trade-offs between the three dimensions of the triple bottom line. As a result, this article presents a framework for guiding corporate decision-processes. From the academic angle, the article contributes to the research field by providing a structured integration of concepts and methods utilizable in the pursuit of ecological and social improvements along the value chain. 
## 
## Topic 5: Sustainability
## V25: 10.1016/j.jclepro.2016.11.041
## With the increasing conflicts among economic growth, environmental deterioration, and resource shortage, as an effective way to achieve the sustainable development and circular economy, the eco-industrial park gradually becomes a critical research issue in the field of recycling economy. In this paper, a hybrid framework for evaluating the comprehensive benefit of eco-industrial parks from the perspective of circular economy was proposed. Firstly, the evaluation index system for eco-industrial parks was constructed by employing the grey-Delphi method according to the opinions provided by experts from relevant fields, which includes economic benefit criteria, social benefit criteria, environmental benefit criteria, ecology industry construction criteria, and management level criteria, consisting of seventeen quantitative sub-criteria and nine qualitative sub-criteria. Secondly, in order to solve complicated, uncertain and ambiguous problems under the fuzzy background, a hybrid multi-criteria decision making (MCDM) approach was put forward on the basis of superiority linguistic ratings and entropy weight method for weight determination as well as fuzzy-VIKOR for ranking alternatives. Finally, the effectiveness and practicality of the proposed hybrid MCDM approach was demonstrated through a case analysis of six representative eco-industrial parks in China, and the comprehensive benefits of six eco-industrial parks were prioritized effectively. The hybrid MCDM approach in this paper presents a great potential in evaluating and ranking the comprehensive benefits of eco-industrial parks through solving complex and ambiguous problems in a fuzzy environment and varying MCDM tools. The evaluation findings by employing the proposed MCDM approach can provide references for the construction and management of eco-industrial parks and relevant policy formulation. 
## 
## Topic 6: Household Energy
## V1: 10.1016/j.rser.2018.07.052
## Given the significance of the residential sector in terms of energy consumption, a comprehensive understanding of households’ energy consumption patterns and choices is imperative. This paper focuses on analysing and understanding the South African residential sector&#39;s energy characteristics considering their energy-use profile, and other characteristics such as their geographical distribution and demographic characteristics. The findings show that despite poorer households who are connected to the national grid receiving 50 kW/h of free electricity per month to help them cover their basis energy needs, South African households – particularly low-income households – still use various sources of energy including wood and paraffin to satisfy their basic energy requirements. Solid fuels are predominantly used in rural areas where around 75% of non-electrified households rely on solid fuels for cooking, heating and lighting. Low-income South African households consume between 5% and 10% of their total energy in lighting; space heating and cooking account for the remainder 85–90% of their total energy consumption. After evaluating the relevant data regarding households’ access to electricity, the type of energy used by households and households’ expenditure on electricity and energy in South Africa from the NIDS dataset; it was concluded, that electricity access is reasonably high across South African households. Additionally, an increasing trend can be observed in their total expenditure on electricity. However, approximately 70% of households still spend on other energy sources. 
## 
## Topic 6: Household Energy
## V2: 10.1016/j.scs.2017.05.011
## Studies for building designs exist both internationally and, to a lesser degree, in the South African context. However, there is limited understanding of the various types of low-cost buildings and their related energy flow patterns, as found within townships and other informal urban settlements within the City of Cape Town. This study thus developed a typology of representative low-cost building types based on their energy profile in two townships in Cape Town: Gugulethu, and Manenberg. The buildings were classified into eight representative types: Rowhouses; Maisonettes; Cottages; Courts; Government Reconstruction and Development Programme houses; Migrant labour hostels (1-storey); Migrant labour hostels (2-storey); and buildings known colloquially as ‘2-storeys’. DesignBuilder and EnergyPlus energy profile simulation results demonstrate that high energy consumption of low cost buildings is due to: poor building orientation; high occupancy rates; non-insulated walls and roofs; lack of ceilings; the use of kettles as a primary source of water heating; and inefficient incandescent lighting. Based on the household appliances and lighting, the results show that Maisonettes have the highest average annual energy consumption per square meter relative to the other building typologies, at 515 kWh/m2/year. However, Migrant labour hostels (1-storey) show the highest annual energy consumption at 16741 kWh/year, based on the household appliances and lighting. Opportunities to improve energy consumption efficiencies in low cost buildings in the two townships relate to both contextual analysis of the building types, and the household lifestyles related to energy use. 
## 
## Topic 6: Household Energy
## V3: 10.1016/j.apenergy.2022.119557
## Low-income households in the United States experience higher than average energy burdens (defined as the proportion of household income spent on energy utilities), and many of these households struggle to simultaneously pay for rent, energy, and basic household necessities. The analysis presented here examines whether the underlying characteristics of buildings and their energy systems could contribute to this disparity for affordable housing residents in New York City. It combines an energy audit dataset of 7,328 multifamily buildings with a database of properties receiving local, state, or federal housing subsidies. The results of this analysis indicate that the building-level installed equipment in large (greater than 50,000 square feet) affordable housing buildings in New York City is more efficient than that in market-rate buildings, but this trend largely disappears when considering overall building characteristics, such as location, size, or age. Significant differences in the types of systems installed in affordable and market-rate housing are also observed, as well as the types of energy efficiency recommendations made by energy auditors. However, these latter data were not normalized by building system characteristics, as that analysis is much more difficult to interpret for categorical data such as heating system type. These findings indicate that retrofit policies and building performance standards focused on affordable housing will likely need to account for underlying differences in building characteristics between affordable and market-rate housing to achieve intended impacts. 
## 
## Topic 6: Household Energy
## V4: 10.1016/j.esd.2016.08.003
## Although most studies of energy poverty focus on whether or not households have access to modern fuels, expenditure is also an important issue, as households in developing countries spend a significant proportion of their total expenditures on energy. Using nationally representative household data from India, 1987–2010, this article describes and explains trends in household energy expenditure. While monthly household spending on energy has increased in many Indian states, this change is not driven by increased household affluence. Statistical analysis shows that when modern fuels (LPG for cooking, electricity for lighting and appliances) are available, households are willing and able to spend on energy. Indian households that have seen improved access to LPG and electricity have also seen much higher energy expenditures, whereas increased household incomes do not explain greater spending on household energy. For policymakers, the key lesson is that programs to improve access to modern fuels allow both wealthy and poor households to spend money on valuable energy services. 
## 
## Topic 6: Household Energy
## V5: 10.1016/j.energy.2022.124613
## A greater proportion of Ghana&#39;s charcoal, which contributes to deforestation, is consume by urban households. To help inform interventions on clean cooking fuel use, this study explored the influence of socioeconomic and demographic factors on urban household choice of cooking fuel (clean vs. unclean) in Ghana, using the 2014 Ghana Demographic and Health Survey, a national representative survey data. The factors explored were sex of household head (HH), age of HH, marital status of HH, household size, educational level of HH and household wealth. Data were analysed through simple percentages, chi-square test and binary logistic regression. A statistically significant association was found between all six socioeconomic and demographic factors, and urban household choice of cooking fuel. However, from the odds ratios, we identified household wealth as the main determinant of urban household choice of clean cooking fuel because clean cooking fuel and equipment are expensive compared to unclean cooking fuel. We recommend that interventions aimed at scaling up urban households use of clean cooking fuel in Ghana and similar environments should include price reduction and/or subsidization of clean cooking fuel and equipment as well as economic empowerment of poor households. 
## 
## Topic 6: Household Energy
## V6: 10.1016/j.egyr.2022.01.089
## Energy consumption of the buildings sector has increased dramatically over the past decade. In Egypt, existing buildings consume 60% of electrical energy. Improving existing buildings through energy retrofitting and using different techniques of integrated PV may serve as a step towards improving the energy performance in buildings in Egypt and achieving nZEB levels. This research aims to explore the potential of achieving nZEB levels through retrofitting office buildings’ typical prototypes in Cairo, Egypt, with basic designs, layouts, and orientations. In this study, 12 hypothetical office buildings (linear, square (courtyard), and L-shaped) with both open and closed plans N-S and E-W oriented were simulated using DesignBuilder. The proposed retrofitting strategy succeeded in decreasing annual energy consumption by 46%–65% for all retrofitted cases. Four retrofitted cases met the nZEB target, these are the closed squared office building oriented E-W, closed squared office building oriented N-S, the closed linear office building oriented E-W, and open-plan linear office building oriented E-W. This study may be considered prototypical to encourage policymakers to urgently enforce existing energy conservation codes and set retrofitting measures, to start a national retrofit process of existing office buildings in Egypt. 
## 
## Topic 6: Household Energy
## V7: 10.1016/j.egyr.2016.01.002
## This study investigates the effect of the presence of fireplaces at the household level independent of the function of ambiance and indoor air quality. The focus of this study is on the winter heating energy use of homes with and without fireplaces to promote energy conservation. Three years of winter energy usage (2011-2013) of 365,190 single-family homes are analyzed and compared. The data is further segmented by fuel type, all-electric versus dual-fuel homes as well as by size and vintage. On average, homes with fireplaces used 23,650 kBtu, source energy, for heating purposes during the winter months versus 18,055 kBtu (p≤0.0001) during the same time period, January, February, and December. There is a significant 31% increase in energy use in homes with fireplaces. In conclusion, policy prescriptions and retrofits are recommended during new home construction permits, renovations, and utility rebate outreach programs to encourage more efficient and cleaner fireplace technology applications. 
## 
## Topic 6: Household Energy
## V8: 10.1016/j.enpol.2018.03.057
## Many national grant aid schemes exist to encourage households to invest in residential energy efficiency retrofits, but these can also be availed of by free-riders, which are households that would invest in a retrofit even in the absence of financial support. We use a McFadden&#39;s choice model to estimate willingness-to-pay for energy efficiency using data from a national residential energy efficiency grant scheme, estimating average marginal willingness-to-pay of &amp; #x20AC;0.127 /kWh/yr for retrofits that affect the efficiency of energy use required for space and water heating (e.g. boiler upgrades, heating controls). The results of this analysis are used to estimate the extent to which free-riding has occurred in the scheme. Less efficient and larger households are willing to pay more for energy efficiency improvements, while households that had previously retrofitted via the scheme were willing to pay over twice as much as those retrofitting for the first-time. Free-riding varies by retrofit measure, with solar collector retrofits possessing close to zero free-riders, while free-riders comprised over 33% of heating controls retrofits. 
## 
## Topic 6: Household Energy
## V9: 10.1016/j.esd.2014.05.005
## This paper presents results of three United States Environmental Protection Agency (U.S. EPA) sponsored field studies which assessed the fuel consumption impacts of household energy programs in Benin, Uganda, and Gujarat, India. These studies expand on a previous round of U.S. EPA supported efforts to build field testing capacity and collect stove performance data in Peru, Nepal, and Maharashtra, India. Daily fuel consumption estimates of traditional and intervention technologies were made using the Kitchen Performance Test (KPT) protocol to determine the potential fuel savings associated with the respective programs. The programs in Benin and Gujarat, India resulted in significant fuel savings of approximately 29% and 61%, respectively. In Uganda, the homes using liquefied petroleum gas (LPG) consumed approximately 31% less charcoal than those not using LPG, although the total energy consumption per household was similar between the baseline and LPG user groups. 
## 
## Topic 6: Household Energy
## V10: 10.1016/j.apenergy.2013.10.041
## Buildings represent the largest sector of primary energy consumption and play a major role in saving energy and reducing greenhouse gas emissions. Our analysis of energy consumption and potential energy savings is based on field measurements, computer simulations and economic calculations. The average primary energy consumption (PE) of wooden apartment buildings was 331kWh/(m2a) 83% higher than the limit 180kWh/(m2a) set in national regulations for apartment buildings subject to major renovation. The studied buildings represent a high potential for energy savings. The renovation packages were compiled using different insulation measures, HVAC solutions and energy sources to achieve a 20-65% reduction of primary energy. For historic buildings, the renovation solutions that concentrate on the building envelope can be problematic due to the need to preserve cultural and architectural values. Our calculation results indicate that the cost optimal PE level is around 250kWh/(m2a) and the point at which renovation packages recover expenses is around a PE level of 170kWh/(m2a). In terms of the architectural appearance the point at which renovation packages recover expenses is around a PE level of 210kWh/(m2a). We propose to set a different PE limit for historic wooden apartment buildings with an architectural appearance worth preserving. © 2013 Elsevier Ltd. 
## 
## Topic 6: Household Energy
## V11: 10.1016/j.enpol.2011.01.046
## In this paper, a detailed simulation-based analysis is conducted to assess the impact of adopting Daylight Saving Time (DST) on the electrical energy use and peak demand in Kuwait. The analysis focused on the impact of DST in the building sector since it represents 90% of electrical energy usage of Kuwait. The simulation results indicate that the adoption of DST has mixed impacts for Kuwait. While the commercial and the governmental sectors may benefit from the DST, the private residences and apartment buildings can see both their annual energy use and peak demand increase slightly by adopting DST. The overall impact of the DST implementation is rather minimal with a slight increase energy use of about 0.07% and a slight reduction in peak demand of 0.14% or about 12. MW based on 2005 electrical peak demand for Kuwait. © 2011 Elsevier Ltd. 
## 
## Topic 6: Household Energy
## V12: 10.1016/j.rser.2019.109469
## European building legislation is establishing increasingly stricter requirements to reduce the energy demand of buildings as a measure to decrease energy use and associated carbon emissions. In order to comply with the new standards, the most impactful parameters are subject to important revisions. Airtightness is revealed as an impacting parameter on air conditioning energy demand for nearly Zero Energy Buildings (nZEB). Currently the Passivhaus standard, taken as a constructive reference for nZEB in Europe, establishes 0.6 ACH as the maximum infiltration at 50 Pa for all new buildings irrespective of the climate zone. Nevertheless, the influence of infiltrations on the energy demand is lower in warm climates. This study estimates the potential heating and cooling energy demand for different levels of infiltration rates in southern Europe. For this purpose, a dwelling equipped with a mechanical ventilation system with a heat exchanger has been simulated in TRNSYS. The calculations have been performed in different cities with different levels of infiltrations. This research provides the information required to set airtightness parameters in residential buildings in southern Europe to satisfy the new requirements for nZEB. 
## 
## Topic 6: Household Energy
## V13: 10.1016/j.apenergy.2022.119765
## Many aspects of the daily lives of those living in the United States were substantially impacted by the COVID-19 pandemic in the year 2020. A broad diversity of measures was implemented to curb the spread of the virus, many of which included adjustments to where and how people worked, went to school, and otherwise conducted their daily lives compared to pre-pandemic times. This has impacted how residential buildings are used, how much time people spend in their homes, and as a result, how much energy these buildings consume. The main objective of this study is to analyze, at a national scale, the differences in the occupancy schedules and activities conducted in homes in the U.S., as compared to pre-pandemic. 15 years of American Time Use Survey and Current Population Survey data, from 2006 to 2020, was used in this study to analyze the occupancy schedules for both pandemic (2020) and pre-pandemic (2006–2019) times. These impacts were also analyzed with respect to variables including, weekday/weekend, month of the year, age of the occupants, household income, and household size. The impact of the pandemic on occupant schedules were most substantial in the initial months, whereas as the months progressed, these occupancy profiles slowly changed. Across 2020, people spent, on average, 8 % more time (1.9 h) in their home on weekdays, and 3–6 % (1.2 h) on weekend days. The percentage of time spent for different activities and locations within homes were also studied. For 1-member households, their time spent at home decreased whereas for 2-, 3-, and 4- member households, they spent more time at home. Overall, people spent around 45% more time doing office- and work-related activities at home compared to pre-pandemic, which is likely due to increased remote working and schooling. This research helps to improve the understanding of the occupancy presence and absence profiles in U.S. residential buildings due to the pandemic and provides new insights as to modified profiles for researchers, building designers, and policy makers. 
## 
## Topic 6: Household Energy
## V14: 10.1016/j.jclepro.2015.05.105
## Energy performance certificate databases are a key tool for mapping national building stock and thus fostering greater overall energy efficiency. This paper presents an insight into the energy performance of residential and tertiary sector buildings in Spain, through an analysis of the first 129,635 energy performance certificates issued for existing buildings, collected by the Catalan Institute of Energy. Most of the residential buildings or building units that were studied were &quot;E&quot; class (53.6%). Single-family houses were found to use more energy on average (248.0 kWhp/m2) than individual dwellings (183.2 kWhp/m2). Tertiary sector buildings were found to have slightly better energy performance (26.4% of buildings were rated &quot;D class&quot;), with an average energy consumption of 317.8 kWhp/m2. Modern buildings consume less energy, as they must meet the higher energy performance requirements stated in thermal building regulations. Residential buildings or building units located in hotter climate zones consume slightly less energy than those located in colder zones, mainly because heating accounts for a high percentage of overall energy expenditure (70-75% in residential buildings). A significant proportion of the energy consumed in tertiary sector buildings is for lighting (37.2%). This research defines the current energy consumption baseline of existing buildings in Spain. The results can help to prioritize energy conservation efforts according to building type, construction period, climate zone and specific end-uses. They may also help public authorities to plan future energy policies, and construction practitioners to identify market segments and business strategies. 
## 
## Topic 6: Household Energy
## V15: 10.1016/j.biombioe.2013.07.017
## Low cost, improved cook stoves can potentially provide multiple benefits for local environments and climate through net reduction in fuel use and emissions from use of biomass fuels compared to traditional stoves. However the fuel efficiency of improved cook stoves has undergone limited in-field evaluation. In this study, we assessed the effectiveness of rocket mud stoves (RMS) in reducing fuelwood use in a rural community in Kenya. Using both cross-sectional and longitudinal (before-after) study designs, we conducted two-day kitchen performance tests in 145 households and 37 households, respectively. Fuel consumption was 5.4kgd-1 in the rocket stove group and 6.7kgd-1 in the three-stone stoves group. After we accounted for number of people cooked for, cooking duration and fuel moisture, RMS use was associated with 1.6kgd-1 (95% CI: 0.6, 2.7) lower fuel use relative to three-stone stoves in the cross-sectional sample, and 2.2kgd-1 (95% CI: 1.0, 3.3) lower fuel use in the longitudinal sample. Our data suggest that simple wood stoves like rocket mud stoves could ease the burden of fuelwood collection for rural households, and potentially address social and local environmental consequences of fuelwood collection.© 2013 Elsevier Ltd. 
## 
## Topic 6: Household Energy
## V16: 10.1016/j.enpol.2019.111085
## This paper addresses the thermal Energy Performance Gap (EPG), defined as the difference between a building&#39;s theoretical and actual energy consumption for thermal purposes (heating and hot water). Successful energy policies require estimates of the energy saving potential of the building stock. It is the objective of this work to analyse whether and to what extent an EPG exists in residential buildings in Switzerland. The database of the Swiss Cantonal Energy Certificate for Buildings was used, covering over 50 000 buildings. The median EPG was found to be −11% (i.e. actual consumption lower than theoretical) but varied across ratings from 12.4% (B-label) to −40.4% (G-label). Buildings with low energy ratings tend to consume significantly less than expected, while buildings with high rating tend to consume slightly more than expected. For the A-labels buildings (0.5% of the total) an EPG of −6.2% was found, suggesting that the very high-performance buildings may be more robust to the EPG. Simplified scenarios to illustrate the impact of this EPG on total consumption are presented, which highlight the challenge of meeting the Swiss Energy Strategy 2050 with a realistic renovation rate. The importance of low carbon heat supply for buildings is also discussed. 
## 
## Topic 6: Household Energy
## V17: 10.1016/j.enpol.2016.07.015
## The register of the Dutch social housing stock was analysed, containing 300.000 dwellings, renovated between 2010 and 2013. The main objective was twofold: to evaluate the performance gap in these dwellings before and after the renovation and to establish what renovation measures achieve the highest reduction of consumption, particularly in practice (actual savings). The results showed large performance gaps in dwellings with low R and high U values, local heating systems, changes from a non-condensing into a condensing boiler and upgrades to a natural ventilation system. Regarding the actual effectiveness of renovation measures, replacement of old gas boilers with more efficient ones yields the highest energy reduction, followed by deep improvements of windows. Installing mechanical ventilation yields a small reduction compared to other measures, but still much larger than theoretically expected. The paper shows once more that the calculation method currently in use cannot be considered accurate if compared to actual consumption. The study demonstrated that unrealistic theoretical efficiencies of heating systems and insulation values are causing a part of the performance gap. Nowadays, large datasets of buildings thermal performance and actual consumption offer an opportunity to improve these misconceptions. 
## 
## Topic 6: Household Energy
## V18: 10.1016/j.energy.2019.116874
## Energy performance of buildings directive sets a goal to achieve a highly energy efficient and decarbonised building stock by 2050. In this study, a pilot nZEB (nearly zero energy building) renovation of an existing apartment building is analysed. nZEB criteria of new apartment buildings was set as the energy performance target in designing renovation solutions. The whole building envelope was additionally insulated with prefabricated modular panels and new service systems were installed. Measured energy consumption after the renovation showed that the pilot building fulfilled the minimum energy performance requirements for new apartment buildings, but nZEB target was not achieved. Measured heating energy consumption is 1.6 times higher (mainly because of the higher indoor temperature, supply air temperature, window airing, and higher ventilation airflow rates which methodology for heating energy calculations are not taken into account) and measured energy need for DHW is 4.4 times higher (mainly because of the real use profiles as well unexpected performance of solar collector and sewerage heat recovery system) than expected in building design. Results show that in renovation projects (also in new projects), occupant behaviour and the real use of building should be used as more realistic input parameters for designing energy performance. Distribution and circulation losses of air handling units, heating coils and DHW (domestic hot water) systems should be taken into account in the national energy calculation methodologies as service system heat losses can be a significant part of energy consumption at nZEB levels. If the renovated building would be used according to design methodology, the nZEB target can be achieved. 
## 
## Topic 6: Household Energy
## V19: 10.1016/j.apenergy.2021.118128
## Brussels is one of the European cities with the most significant number of Passive House buildings on the continent. In the Brussels-Capital Region, the nearly zero-energy building obligations implemented is implemented since 2010. The Brussels-Capital Region has set up ambitious energy standards for new constructions. These standards target &#39;nearly zero&#39; or &#39;very low energy consumption and are inspired by the &#39;passive house standard,&#39; where high-energy performance is first achieved. Ten years after boasting this groundbreaking policy, many renovated, terraced houses are renovated to comply with the nearly zero-energy building requirements. Therefore, this study aims to develop an energy performance data set and one building performance simulation benchmark model for nearly zero-energy dwellings in Brussels. The study reports an inventory and field survey conducted on a terraced house renovated after the year 2010. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted. A building performance simulation model is created in EnergyPlus to benchmark the average energy consumption and building characteristics. The estimate&#39;s validity has been further checked against the public statistics and verified through model calibration and utility bill comparison. The benchmark has an average energy use intensity of 29 kWh/m2/year and represents terraced single-family houses after renovation. The paper provides a timely opportunity to evaluate the actual performance of nearly zero-energy terraced houses. The findings on energy needs and use intensity are useful in temperate and continental climates. 
## 
## Topic 6: Household Energy
## V20: 10.1016/j.enpol.2012.11.008
## In Europe, the Energy Performance of Buildings Directive (EPBD) provides for compulsory energy performance certification (labelling) for all existing dwellings. In the Netherlands, a labelling scheme was introduced in 2008. Certificates contain the energy label of the dwelling and corresponding theoretical gas and electricity consumption, calculated based on the dwellings physical characteristics, its heating, ventilation and cooling systems and standard use characteristics. This paper reports on a large-scale study of around 200,000 dwellings comparing labels and theoretical energy use with data on actual energy use. The study shows that dwellings with a low energy label actually consume much less energy than predicted by the label, but on the other hand, energy-efficient dwellings consume more than predicted. In practice, policy targets are set according to the theoretical rather than the actual consumptions of the building stock. In line with identified discrepancies, the study shows that whereas most energy reduction targets can be met according to the theoretical energy consumption of the dwelling stock, the future actual energy reduction potential is much lower and fails to meet most of the current energy reduction targets. © 2012 Elsevier Ltd. 
## 
## Topic 6: Household Energy
## V21: 10.1016/j.energy.2015.09.016
## Millions of homes around the world suffer from &quot;fuel poverty,&quot; commonly defined as the necessity to spend more than 10 percent of their income paying energy bills. This article first discusses how home energy efficiency schemes, such as those that pay to weatherize doors and windows, install insulation, and give free energy audits, can significantly reduce the prevalence of fuel poverty. It then examines the &quot;Warm Front&quot; program in England, which over the course of 2000-2013 saw 2.3 million &quot;fuel poor&quot; British homes receive energy efficiency upgrades to save them money and improve their overall health. Warm Front not only lessened the prevalence of fuel poverty; it cut greenhouse gas emissions, produced an average extra annual income of £1894.79 per participating household, and reported exceptional customer satisfaction with more than 90 percent of its customers praising the scheme. This study details the history, benefits, and challenges of the program, and it teases out six noteworthy lessons for energy analysts, planners, and policymakers. 
## 
## Topic 6: Household Energy
## V22: 10.1016/j.enpol.2020.111323
## To date, research has mostly focused on the impact of energy efficiency on the total electricity demand but not on the electricity demand profiles. To address this gap, we estimate the impact of energy efficiency measures and policies such as minimum energy performance standards on the peak load by developing a bottom-up model that generates Swiss household hourly electricity demand profiles per appliance based on time use data. The model estimates that evening appliance peak demand can be reduced by 38% when the appliances are replaced by the highest energy efficiency label available on market. We find that changing light bulbs to LED would have the same peak reduction as switching cooking or wet appliances to off-peak periods throughout the year. We also show that the evening appliance peak demand could reduce in 2035 by 24% thanks to the improvement of the energy performance of the stock. Cooking appliances, the least favourable appliances to be involved in demand response, is expected to be the highest contributors to the evening peak in 2035. Our findings show that policy makers should pay due attention to energy efficiency improvement not only for reducing electricity demand but also in order to reduce peak load. 
## 
## Topic 6: Household Energy
## V23: 10.1016/j.applthermaleng.2015.10.100
## A Net-Zero Energy Residential Test Facility (NZERTF) has been constructed at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland, to demonstrate that a home similar in size, aesthetics, and amenities to those in the surrounding communities can achieve net-zero energy use over the course of a year while meeting the average electricity and water use needs of a family of four in the United States. The facility incorporates renewable energy and energy efficient technologies, including an air-to-air heat pump system, a solar photovoltaic system, a solar thermal domestic hot water system, and a heat recovery ventilation system sized to meet American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) Standard 62.2-2010 ventilation requirements. The largest energy end use within the home was space conditioning, which included heat loss through the building envelope, ventilation air supplied by the heat recovery ventilator (HRV), and internal loads. While HRVs are often described as being able to save energy when compared to ventilating without heat recovery, there have been no studies using a full year of measured data that determine the thermal load and energy impacts of HRV-based ventilation on the central heating and cooling system. Over the course of a year, continuous operation of the HRV at the NZERTF resulted in an annual savings of 7% in heat pump energy use compared with the hypothetical case of ventilating without heat recovery. The heat pump electrical use varied from an increase of 5% in the cooling months to 36% savings in the heating months compared with ventilation without heat recovery. The increase in the cooling months occurred when the outdoor temperature was lower than the indoor temperature, during which the availability of an economizer mode would have been beneficial. Nevertheless, the fan energy required to operate the selected HRV at the NZERTF paid for itself in the heat pump energy saved compared with ventilation without heat recovery. 
## 
## Topic 6: Household Energy
## V24: 10.1016/j.scs.2019.101534
## The socioeconomic reality of the social housing neighborhoods in Southern European countries imposes the application of passive energy rehabilitation schemes to minimize the thermal discomfort. A program to evaluate the indoor climate using experimental measurements was developed during one year, in a social housing neighborhood in Porto, Portugal. The results allowed validating a dynamic numerical simulation model for a reference dwelling, to assess thermal comfort in eight cities of Portugal and Spain. The study allowed concluding that for social housing buildings without heating or cooling systems installed, it is possible to have thermal comfort for a scenario without additional thermal insulation in facades, considering the remaining construction elements well insulated. During winter, in Lisbon, Faro and Seville, the percentages of thermal discomfort are less than 8% for the category III of EN 15251:2007. In summer, the thermal comfort may be achieved in Porto and Bilbao, since the percentage of thermal discomfort is negligible. The small reduction (13%) on thermal discomfort with the increasing of the thermal insulation in facades makes evident that it is impossible to have thermal comfort in Bragança, Madrid, Bilbao and Porto without heating the buildings and in Madrid, Barcelona, Seville and Faro without cooling the buildings. 
## 
## Topic 6: Household Energy
## V25: 10.1016/j.enpol.2014.11.021
## This study uses high-frequency appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The Results show that households&#39; estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 75% and underestimate plug-load use by 29%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. For example, households in the 75th percentile of HVAC usage use over four times as much electricity as a user in the 25th percentile. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more energy-efficient model leads to overall energy savings of approximately 11%. This is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based energy conservation policies. 
## 
## Topic 7: Consumers
## V1: 10.1016/j.spc.2017.11.005
## Over-consumption and a throwaway culture contribute to increased textile waste, which is a growing environmental concern. Collaborative consumption may encourage the reuse of products and reduce new purchases to prevent excessive textile waste. The purpose of this study was to identify the influence of personality traits on consumers’ intention to engage in collaborative consumption through clothing renting and swapping. By applying the theory of planned behavior (TPB), this study considers that engaging in collaborative consumption is not only from an individual&#39;s inner characteristics but also from self-interest and social values. An online survey was conducted with 431 US females. Structural equation modeling (SEM) was used to test proposed hypotheses. The results indicated that three personality traits, including fashion leadership, need for uniqueness, and materialism, significantly influence the intention to rent and swap clothing. Further, personality indirectly influences the intention to adopt collaborative consumption through attitude, perceived behavioral control, and past sustainable behavior. This study highlights the role of personality traits, attitude, perceived ability and past experiences on collaborative consumption, and provides guidance and suggestions for clothing renting and swapping businesses. 
## 
## Topic 7: Consumers
## V2: 10.1016/j.jclepro.2017.02.187
## Using the theory of consumption values, this research proposes to explore the consumer choice behavior for green products in Pakistan. Functional value (price and quality), social value, conditional value, epistemic value, and emotional value and environmental value were used to study the consumer choice behavior for green products. It also gauges the extent to which emotional value moderates the impact of other consumption values on green product consumer choice behavior. Based on a sample of 260 respondents, the results indicate that functional value (price), social value and environmental value have a positive impact on green product consumer choice behavior, while conditional value and epistemic value have a negative effect. Functional value (quality) and emotional value do not influence green product consumer choice behavior. As a moderator, emotional value has a significant effect on the role of functional value, social value, conditional value, epistemic value and environmental value. This confirms and significantly adds to the literature of green product consumer choice behavior in a developing market. 
## 
## Topic 7: Consumers
## V3: 10.1016/j.jclepro.2020.120406
## Adoption of green information technology (Green IT) as an initiative of pro-environmental behavior is quite sparse among Malaysian students. Due to the need to integrate personality traits in environmental behavioral studies, the current study deriving from the planned behavior theory attempts to investigate the influence of attitudinal factors on students’ pro-ecological behavioral intention to practice Green IT. Extremely few studies explored the moderating role of personality traits in the context of Green IT adoption and thus this study attempts to fill the current research gap. The personality traits of openness, agreeableness and conscientiousness were included in the research model as moderating variables. A number of 262 pieces of data were collected from students. Based on the partial least squares approach and bootstrapping method, the results revealed that except social norms, other variables greatly influenced the intention to practice Green IT. Moreover, the moderating effects analysis showed that the personality trait of conscientiousness was the only trait that significantly moderated the relationships in the proposed model. 
## 
## Topic 7: Consumers
## V4: 10.1016/j.spc.2022.09.026
## Natural claims have been increasingly used by brands across a variety of product categories to address the growing concerns about sustainable and healthy consumption. To add insights to this body of knowledge, this research aims to investigate the influence of natural claims on consumers&#39; judgments and purchase intentions of personal care products. Findings from two studies suggest that natural claims are broadly used in personal care product packaging to influence consumers&#39; purchase intentions, due to the natural-is-better bias and the health halos evoked by such claims. This research also contributes to the literature by investigating the underlying mechanisms of perceived efficacy, safety, sensorial expectations, and greenwashing perceptions. Moreover, environmental consciousness moderates the effects of natural claims on consumers&#39; judgments of perceived efficacy. The findings thus not only enhance our understanding of the natural-is-better bias but also shed light on the role played by perceived safety and sensorial expectations on intentions to purchase natural-claimed products. Relevant implications for brands and policymakers in terms of sustainable consumption are also discussed. 
## 
## Topic 7: Consumers
## V5: 10.1016/j.jclepro.2023.137948
## This quantitative survey-based study is based on the theory of consumption values and the value-motivation-behavior association. It explores how craft brewery owner - manager’ consumption values of green products in their personal lives influence the adoption of environmental practices in their businesses. The study also investigates the relationship between the motivation to adopt environmental practices (MAEP) and the actual adoption of such practices (AEP) in craft breweries. In addition, the study examines the impact of perceived business challenges (PBC) on the motivation-adoption relationship. Results from 236 craft brewery owner-manager from the Brewers Association, employing multiple regressions, showed that four consumption values: social, emotional, conditional, and epistemic, significantly influenced MAEP in their craft breweries. However, only emotional, and conditional values influenced MAEP positively, as hypothesized. Results also confirmed a significant positive relationship between MAEP and AEP. Furthermore, PBC significantly weakened the relationship between motivation and adoption. Based on the findings, numerous takeaways are offered for related stakeholders. 
## 
## Topic 7: Consumers
## V6: 10.1002/sd.2426
## Educators, marketers, and policymakers need to understand what factors influence the consumers&#39; purchase of green products to encourage green product buying. Thus, this study examines the antecedents to green purchases. An online survey provides quantitative input for a SmartPLS analysis. The study draws on the theory of consumption values to investigate how emotional and social values affect green purchasing decisions. The role of knowledge-seeking in developing these values is investigated and found to be significantly positive. The relationships between emotional value, social value, and green purchase behavior are found to be mediated by environmental concerns. This study contributes to the discussion around consumption values by showing that environmental concern has a stronger relationship with green purchases than emotional and social value. It is through the environmental concern that emotional and social values influence green purchases. Our findings suggest that marketers should leverage the consumers&#39; knowledge-seeking and environmental concern in their advertising campaigns by emphasizing a specific green product novelty and highlighting the casual connection between green buying and their effect on the environment. 
## 
## Topic 7: Consumers
## V7: 10.1016/j.jclepro.2021.128995
## This study examined the impact of eco-brand, eco-label and social media on driving green consumption intention in ecotourism destinations. The mediating influence of motivation and the moderating impact of environment concern on all proposed relationships were also investigated. Data were collected by using a structured questionnaire survey at different eco-locations. The research uses the data from 341 valid observations collected in the survey of 550 visitors. The findings indicated the significant association of eco-brand and eco-label with green consumption intention. Eco-brand was revealed as the most important factor impacting green consumption intention. However, the relationship between social media and green consumption intention was mediated by motivation and moderated by environment concern. This study contributes to theoretical and practical implications by proposing the green behavioral model. 
## 
## Topic 7: Consumers
## V8: 10.1016/j.jclepro.2023.139506
## This study examines how risk communication and risk perception related to the pandemic affect tourists&#39; intention to engage in pro-environmental travel behavior. An online survey was conducted, yielding 449 valid responses. The data was analysed using Partial Lease Square – Structural Equation Modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results indicate that risk communication and cognitive risk perception significantly impact pro-environmental travel behavioral intention (PETBI). Although the output of PLS-SEM does not support the positive effect of affective risk perception on PETBI, the results of fsQCA support this relationship. Additionally, this study confirms the mediating role of cognitive risk perception between risk communication and PETBI. Environmental responsibility is found as a significant mediator between affective and cognitive risk perception and environmental moral obligation, while environmental concern is established as a significant mediator between cognitive risk perception and environmental moral obligation. Although social influence did not moderate the relationship between environmental moral obligation and PETBI, based on the results of the fsQCA, it can positively influence PETBI. These findings provide valuable insights for the tourism industry in China and other countries, enabling them to develop effective strategies to address risk communication and perception of the pandemic and promote pro-environmental travel behavior. Furthermore, the study offers theoretical and managerial implications for scholars and practitioners alike. 
## 
## Topic 7: Consumers
## V9: 10.1016/j.jclepro.2020.124192
## The shift of consumers buying preference toward green beauty products can alleviate environmental degradation to a certain extent. This study investigated the factors that could affect non-green consumers purchase intention of green beauty products. Through the lens of non-green consumers of green beauty products, the norm activation theory was employed as the underpinning theory in this paper. A quantitative method, based on the partial least squares structural equation modelling, was used to verify the proposed hypotheses. The findings displayed that norm activation theory activators, such as ascription of responsibility, efficacy, social norm and environmental corporate social responsibility (ECSR) initiatives showed a significant impact on personal norm, while awareness of consequences had no influence on personal norm as an activator. Corresponding results disclosed that awareness of consequences, efficacy, social norm, ECSR initiatives, and personal norm exhibited a significant impact on the purchase intention of green beauty products. The study further assessed the effectiveness of personal norm as a mediator variable and found that personal norm mediated four out of five activators of non-green consumers’ purchase intention of green beauty products. According to the research, a further inclusive analysis can be conducted in the future to evaluate the purchase intention of green beauty products among non-green consumers and existing consumers on the foundation of norm activation theory. 
## 
## Topic 7: Consumers
## V10: 10.1016/j.spc.2022.05.015
## Prior studies of pro-environmental behavior investigated its antecedents from the perspective of either rational choices or moral norms. However, as highlighted by many researchers, green purchase behavior associated with high-cost products such as green computers can be influenced by both perspectives. Thus, this study aims to predict the influence of the theory of planned behavior&#39;s (TPB) rational choice factors and the value-belief-norm (VBN) theory&#39;s moral norm factors on the green computer purchase behavior (GCPB) of consumers in the Malaysian context. A survey questionnaire was developed and administered to 1000 Malaysian respondents through convenience- and purposive-sampling methods. The structural equation modeling (SEM) function of the AMOS software was used to analyze the survey data and TPB factors (subjective norms), were found to be more important than the VBN factors (personal norms), in predicting GCPB. This implies that Malaysian consumers have a stronger tendency to follow social norms than fulfill moral obligations when deciding to buy green computers. Additionally, biospheric and altruistic values were drivers for the rational choice model while biospheric values drove the moral norm model. The results of this research provide recommendations to marketers, governments, environmental NGOs, educators, and manufacturers on how to communicate and engage with consumers to promote sustainable consumption through green computer purchases. The study&#39;s originality is in the development and use of a comprehensive integrated model that provides an understanding of consumer behavior when faced with moral-norm and rational-choice decisions in the purchase of a high-cost product in an emerging market context. 
## 
## Topic 7: Consumers
## V11: 10.1016/j.jclepro.2018.01.250
## Research on the word-of-mouth intention of consumers has received increasing attention in hospitality research. However, little research has explored the effect of green image on the word-of-mouth intention of consumers and how this effect was mediated by green satisfaction and green trust. Based on the Stimulus-Organism-Response (S-O-R) framework, this research examined the relationship between green image and the word-of-mouth intention of consumers in the Chinese green hotel industry and explored the mediating effects of green satisfaction and green trust. Considering that Millennials (a large and influential generation born in the 1980s and late 1990s) are extremely concerned about green issues, more concerned about the environment than previous generations, and a powerful source of word-of-mouth about products and services, this research also examined the moderating effect of Millennials. The questionnaire survey method was used to collect data from 324 respondents with the help of travel agencies to test these relationships. The results indicated that the green image of green hotels strongly influences consumer green satisfaction and green trust, and consumer green satisfaction has a significant effect on green trust. The consumers’ green satisfaction and green trust are positively related to their intention to recommend green hotels to individuals around them. Furthermore, significant moderating effects of Millennials were found in all paths between the investigated variables in the proposed model. The results also showed that female tourists are more likely than male tourists to recommend green hotels. The implications of these findings were discussed, and limitations and future research directions were suggested. 
## 
## Topic 7: Consumers
## V12: 10.1002/sd.2147
## Despite there are several contributions on the relationship between various aspects of consumer behavior and environmental awareness, further research is required to analyze consumer behavior in the domain of cigarette butts littering. The aim of this study is to examine the role of feared self and environmental awareness constructs as significant antecedents of consumer behavior, as well as the impact of congruity between feared self and environmental awareness. This study utilizes structural equation modeling (SEM). Specifically, confirmatory factor analysis (CFA) is used to test the construct measures of the structural model and path model analysis to test the hypotheses of the structural model. The hypothesized model was tested using primary data. The main findings are consistent with the hypothesized model and highlight a positive influence of feared self on cigarette butts littering behavior, whereas congruity of feared self and environmental awareness do not have an impact on cigarette butts littering behavior. These results may draw the attention of practitioners, academicians, and policy-makers for environmentally responsible guidance as well as consumer awareness programs for sustainable development. 
## 
## Topic 7: Consumers
## V13: 10.1016/j.jclepro.2018.06.293
## Previous study has confirmed the critical determinants that influence green travel intention. However, these studies rarely discuss the interaction effects of norms and attitudes on green travel intention. In the current paper, this gap would be addressed by using an extended model of the theory of planned behavior (TPB), which (1) differentiated social norms into subjective injunctive norm and subjective descriptive norm, (2) differentiated attitudes into experiential attitude and instrumental attitude, and (3) incorporated personal moral norm. The model is empirically investigated by online questionnaire survey data collected from 419 respondents in eastern China. The findings reveal that the interaction terms of experiential attitude and norms are positively associated with green travel intention, whereas the interaction terms of instrumental attitude and norms are negatively related to green travel intention, which implies that two aspects of attitude play a critical role in motivating green travel intention. Furthermore, subjective descriptive norm can increase individual&#39;s green travel intention when they hold low environmental responsibility. Based on the results, implications for improving individual&#39;s green travel intention, limitations of the research and suggestions for further study are discussed. 
## 
## Topic 7: Consumers
## V14: 10.1016/j.jclepro.2017.08.214
## Corporate social responsibility is gaining significance in the business world. However, scholars haven&#39;t sufficiently examined the factors that influence the small, everyday sustainability behaviors that individual employees might choose to perform. This study had the aim to unravel factors that affect pro-environmental behaviour (PEB) of individual employees. In addition to the known factors of the theory of planned behaviour (attitude towards PEB, subjective norms, perceived behavioural control, and intention to act), factors as leadership support, perceived organisational support for the environment (POS-E) (taken together as institutional support), and leadership (exemplary) behaviour were taken into account. Although the relationship between intention to act and PEB was not significant in this study, based on the findings it can be concluded that leadership behaviour (as exemplary behaviour) and POS-E or in other words the perceived organisational support to act proenvironmentally friendly, are affecting both intention to act and PEB. It is remarkable that leadership support does not affect the intention to act and actual PEB. 
## 
## Topic 7: Consumers
## V15: 10.1016/j.jclepro.2015.10.008
## The genetically modified food industry may contribute to environmental protection and sustainable development. Nevertheless, many consumers are skeptical about genetically modified foods and fear that their diffusion may have detrimental effects on the environment and public health. Given this situation, genetically modified food producers may benefit from understanding how to address such concerns through appropriate corporate social responsibility initiatives. However, there is scarce research investigating this issue. This paper contributes to this research stream by studying how consumers&#39; perceptions about genetically modified food producers&#39; corporate social responsibility initiatives impact said consumers&#39; attitudes toward and intentions to buy such products. This research builds on the wellestablished model of corporate social responsibility proposed by Carroll (1979) and investigates this issue through a survey study of 260 Italian consumers. The results show that perceptions about producers&#39; philanthropic and legal responsibilities favorably impact Italian consumers&#39; attitudes toward genetically modified foods and their intentions to buy such products, respectively. Managers interested in developing the genetically modified food market could therefore focus on these responsibilities to foster favorable attitudes and intentions toward genetically modified foods. 
## 
## Topic 7: Consumers
## V16: 10.1016/j.jclepro.2020.123007
## Human behavior has varying effects on the environment because of its way and intensity. The majority of the current studies on tourists’ environmental behavior primarily considers the environmentally conservative behavior and the environmentally disturbing behavior, while the environmentally radical behavior (very strong sense of responsibility) has not been well-documented in the literature. This study aims to examine the dimensions of tourists’ environmental behavior and the mechanism of different types of tourists’ environmental behavior. Study results identify three dimensions of tourists’ environmental behavior, including: 1) environmentally radical behavior, 2) environmentally conservative behavior, and 3) environmentally disturbing behavior. This paper uses the theory of planned behavior to develop three different interpretation models of tourists’ environmental behavior, and test hypothesized relationships in the interpretation models by structural equation models. The results show that the hypothesized relationships between attitude, subjective norms, perceived behavioral control, environmental behavioral intention and environmental behavior in the tourists’ environmentally conservative behavior and environmentally disturbing behavior interpretation model were all supported. However, the hypothesis that subjective norms positively affect the environmental behavioral intention was not supported in the model of tourists’ environmentally radical behavior. Study results provide suggestions and guidance for tourism management agencies to implement scientific and effective tourism environmental protection measures and policies. 
## 
## Topic 7: Consumers
## V17: 10.1016/j.jclepro.2020.124250
## Food waste has been receiving much scholarly attention from the academic domain, non-profit organizations and governments as the environmental damages and financial losses it causes increase each day, prompting the decision-makers to take preventive measures. Most of the food waste occurs at the household-level, so understanding the causes of consumer-generated food waste will be more useful for prevention. This study aims to investigate both direct and indirect effects of consumers’ moral attitudes, knowledge of food conservation, eating habits, and shopping habits on food waste behavior. Structural equation modeling is employed to survey data gathered from 328 participants in Turkey. The results of the study reveal that moral attitudes have a direct effect on food waste behavior, eating habits, shopping habits, and knowledge of food conservation. Furthermore, shopping habits are shown to have a significant impact on food waste behavior. The analysis also provides evidence of indirect effects such that shopping habits mediate the relationship between moral attitudes and food waste behavior along with the relationship between knowledge of food conservation and food waste behavior. Moreover, the relationship between moral attitudes and shopping habits is mediated by knowledge of food conservation. The results demonstrate that moral attitudes and shopping habits are two important determinants of food waste. When individuals believe throwing away food is wrong and doesn&#39;t match with their self-image, the amount of food they waste decreases. Also, individuals who shop responsibly, and buy as much as they need, report less food waste. Efforts that aim to reduce food waste in households should focus on fostering strong moral attitudes against food waste and warning consumers about the negative consequences of excessive shopping. 
## 
## Topic 7: Consumers
## V18: 10.1108/SAMPJ-11-2019-0405
## Purpose: This study aims to test for factors affecting environmental sustainability and purchase intention in the fashion industry. Accordingly, the authors developed a framework that depicts the relationships between perceptions of social responsibility, consumer attitude, trust, purchase intention and perceived consumer effectiveness. Design/methodology/approach: An online survey was conducted with an internationally diverse sample of 216 consumers. Data were analysed using partial least squares structural equation modelling. Findings: The results indicated that perceptions of social responsibility directly affect consumers’ attitudes towards these fashion brands, as well as trust and perceived consumer effectiveness. Also, consumers need to perceive sustainability efforts of these brands as altruistic, and trust was found to be a direct predictor of purchase intention. However, both consumer attitude and perceived consumer effectiveness did not predict purchase intention. Research limitations/implications: The survey was primarily distributed to young people. Therefore, a generalisation of the findings to other age groups might be limited. Practical implications: Practicing managers should emphasise the fact that environmental sustainability and fast fashion brands could be sustainable to increase trust among consumers. Social implications: When it comes to environmental issues, positive perceptions regarding the companies’ social responsibility efforts are vital to enhance both consumers’ trust towards the brands and their individual feeling of empowerment. Originality/value: This study intends to shed light on the key elements that shape consumers’ attitudes and willingness to purchase green apparel. 
## 
## Topic 7: Consumers
## V19: 10.1016/j.jclepro.2019.119870
## This study is aimed at evaluating the relative influence of socio-demographic and psychological features that rule the extent to which consumers engage in the circular economy, purchasing waste-to-value (WTV) food enriched with ingredients otherwise wasted in the supply chain. 477 Italian consumers replied to a web-based questionnaire administered through different social media networks. Two different consumers’ purchase intentions were analysed: consumers were asked both if they would be willing to buy WTV food and if they would buy WTV food if this would help to reduce the environmental impact of agricultural production. Binary logistic regressions are estimated to appraise the eventual drivers of consumers’ statements. Among these drivers, attention was given to aspects related to the generalised aversion to new foods, i.e. food neophobia (FN) and the aversion to food processed in new ways, i.e. food technology neophobia (FTN). Other relevant economic and demographic factors were investigated, together with aspects related to generalised trust, purchase behaviours and preferences. The main results indicate that 56% of respondents declared to be willing to buy WTV food, however, FN and FTN negatively influence the probability of stating a positive purchase intention. Consumers who give importance to reading food labels and think that food could have environmental or health benefits, are more likely to be willing to buy WTV food. In addition, a core of sustainable consumers seems to emerge who express a positive purchase intention for WTV food to reduce the environmental impact of production and give importance to the origin and nutritional values of products. In conclusion, policy implications are drawn. 
## 
## Topic 7: Consumers
## V20: 10.1016/j.jclepro.2019.03.237
## The adoption of Green Information Technology (GIT) is important to ensure organizations’ environmental performance through sustainable production, consumption, utilization and disposal of Information Technology (IT) devices. However, research on the adoption of GIT practices has mostly addressed organizational factors and outcomes, with limited emphasis on the cognitive and attitudinal factors associated with behavioral change. Based on the Belief-Action-Outcome (BAO) framework, this research examined the effects of individual, social and organizational factors on GIT attitude among a sample of IT professionals in ISO 14001 certified IT companies in Malaysia and investigated the mediating effects of their beliefs about GIT. Further, this research investigated the relationship between GIT attitudes and behavioral change, as indicated through self-reported engagement in green computing practices. Survey methods were used to collect data from 333 respondents. The results support the direct effects of GIT knowledge, social influence and green management culture on GIT attitude. However, hypothesized indirect effects through the mediation of GIT beliefs were supported for GIT knowledge and social influence, but not for green management culture. The relationship between GIT attitude and engagement in green computing practices was supported. The implications of these results are discussed, and future research directions are suggested. 
## 
## Topic 7: Consumers
## V21: 10.1016/j.spc.2022.12.019
## In order to explore a path benefiting sustainable consumption, this article sets out to study the factors associated with consumers&#39; single-use (SU) product reduction using an extended model based on the theory of planned behavior (TPB). We analyze 1680 questionnaires collected via the Environment module of the 2020 Taiwan Social Change Survey that is characterized by a self-reported behavior-intention gap (BIG) found in the data set. Considering the heterogeneity of the BIG, the estimated results of partial least squares structural equation modeling (PLS-SEM) show that the extended TPB model provides a good explanation of the consumers&#39; intentions except for the subgroup of respondents with a large BIG. This finding suggests that, when using theories of reasoned action such as the TPB for research, one should consider the heterogeneity of respondents to draw effective policy implications for each group. As for those respondents with a non-discernible or small BIG, increasing consumers&#39; perceived behavioral control, such as reducing the inconvenience of applying SU substitutes, plays an important role in enhancing the consumers&#39; intentions to reduce their usage of SU products. It is noteworthy that SU recyclable or SU biodegradable substitutes may be viewed by some consumers as environmentally friendly and therefore weaken their intentions to reduce the use of SU products. This greenwashing effect is evidenced by the results for the respondents with a medium BIG. The above difference in the greenwashing effect is not discovered if the heterogeneity of the BIG is not taken into consideration. 
## 
## Topic 7: Consumers
## V22: 10.1016/j.jclepro.2013.02.038
## Pressure for companies to be accountable to stakeholders has encouraged companies to report information on employees, products and the environment. However, it costs companies time, energy and money. Consequently, such information should be of value to corporate preparers and stakeholders. Accordingly, the primary objective of the study is to examine the importance of environmental information in the decisions of fund managers. The normative pressure of institutional theory was used to explain the findings. More specifically, we argue that the emphasis of companies&#39; bottom line in the education and professional training received by fund managers may influence their perception on the usefulness of environmental information in their decision to invest. The survey method was used to collect the data. The results reveal that fund managers rated many environmental items as important. Additionally, environmental information is viewed as important to be disclosed when it affects the company&#39;s future financial performance and this, in turn, will influence stakeholders&#39; decisions. More importantly, fund managers believe that environmental information disclosure should be mandatory for all companies in Malaysia. Despite the lack of emphasis on social and environmental issues on the education and professional training of fund managers, the results revealed that they still perceive environmental information to be useful. Accordingly, the normative isomorphism of institutional theory is not supported. © 2013 Elsevier Ltd. All rights reserved. 
## 
## Topic 7: Consumers
## V23: 10.1016/j.jclepro.2017.12.054
## Providing information to consumers is crucial to foster pro-environmental attitudes and the purchasing of green products. To date few studies explored the interplay between information, pre-existing environmental concern and barriers to the purchase of green products. By analyzing the data from a survey to a large sample of Italian consumers (n = 8001), we advanced 6 hypotheses to explain the main drivers behind consumers’ selection of sustainable products. Attitudes towards green products were the main predictors of green product purchasing and were influenced by consumer&#39;s attitudes towards ecolabels, whose marginal effect varied according to the level of individual environmental concern. Our study provides evidence that ecolabels can play a significant role in attitude formation about green products, interacting with environmental concern. Therefore, we suggest future studies about green consumer profiling based on how consumers access and integrate information in decision-making, to improve green marketing campaigns. Product information strategies are crucial to foster pro-environmental attitudes and the purchasing of green products. To date, few studies have explored the interplay between information, existing environmental concern and barriers to the purchase of green products. By analyzing the data from a survey to a large sample of Italian consumers (n = 8001), six hypotheses to explain the main drivers of consumers’ selections of sustainable products were advanced. Attitudes towards the products were the main predictors of green product purchasing and were influenced by consumer&#39;s attitudes towards ecolabels, whose marginal effect decreased as the environmental concern increased. This research also demonstrates that a complex interaction between previous environmental knowledge and the use of green labels influence attitudes towards sustainable products, rather than environmental concern per-se, and that ethical aspects of production are important predictors of consumer&#39;s attitudes towards sustainable products, despite they have not been traditionally regarded as such. Future studies about green consumer profiling based on how consumers access and integrate information in decision-making are suggested, to improve green marketing campaigns. 
## 
## Topic 7: Consumers
## V24: 10.1108/SAMPJ-06-2021-0221
## Purpose: This study aims to investigate the influence and impacts of stakeholders on the awareness and attitudes towards environmental management practices (EMPs) among hotel managers in Malaysia. Design/methodology/approach: A total of 159 hotel managers participated in the survey. Structural equation modelling using the partial least squares (PLS) technique was used to test the hypotheses. Findings: Owners and regulators influence hotel managers&#39; environmental awareness and attitudes and their adoption of EMPs. Managers with a greater environmental awareness are more likely to adopt basic EMPs, while those with a greater environmental attitude are more likely to adopt advanced EMPs. In addition, stakeholder influence on managers&#39; awareness and attitudes differs for hotels with and without an environmental policy. Research limitations/implications: Other types of accommodation and stakeholders, demographic variations of hotels and different data collection methods could provide additional insights into the hotel sustainability issue. Practical implications: Coercion may be needed to translate hotel managers&#39; environmental awareness and attitudes into practices. Therefore, regulators should provide rules and penalties to enforce mandatory requirements and incentives to encourage environmental sustainability initiatives. Social implications: The joint effort among stakeholders could create a societal norm that appreciates and maintains a sustainable environment and tourism industry. Originality/value: This study emphasises the importance of stakeholder salience theory to understand the association between stakeholder influence on managers&#39; awareness and attitudes and the adoption of EMPs by hotels in Malaysia. It is one of only a handful of studies that focuses on stakeholders&#39; influence on environmental stewardship from managers&#39; perspectives. 
## 
## Topic 7: Consumers
## V25: 10.1016/j.jclepro.2022.135543
## Purchasing green products is one of the cornerstones to promote sustainable development since the negative impact of these products is lower than traditional goods. Consumers’ behaviour towards green products is affected by many factors, including environmental awareness, and might be different among demographic groups. The study aims to test the effect of Environmental Consciousness and the moderation with demographic variables on Intention of Purchase of greener smartphones. 228 consumers from the Southern Brazil answered a questionnaire with items regarding these two constructs. Results indicate a positive relation between them and the relevance of education on the purchase of greener smartphones. This work contributes to the literature with relevant data about environmental awareness among different societal groups, which can help companies and governments to better address environmental campaigns in order to promote sustainable consumption. 
## 
## Topic 8: Ecology
## V1: 10.1088/1748-9326/ac13ee
## National forests in the western United States are divided roughly in half between lands without roads managed for wilderness characteristics and lands with an extensive road system managed for multiple uses including resource extraction. We investigated the influence of these land use designations on fire ignitions, fire extent, and fire severity over the last three decades. Although roadless areas experienced fewer fire ignitions and are generally cooler, moister, and higher elevation landscapes less conducive to fire, wildfire extent was far greater in these areas than in roaded areas. An area equivalent to approximately one-third of roadless areas burned in the last three decades, while an area equivalent to less than one-fifth of roaded areas experienced fire. Most of the largest fires that have burned on national forest land in recent years began in roadless areas. Despite greater fire extent in roadless areas, there was no significant difference in fire severity between roadless areas and roaded areas after accounting for biophysical differences between these management regimes. Although fire patterns in roadless areas may pose challenges to land managers, the available evidence suggests that the greater extent of fire in roadless areas may confer resilience to these landscapes in the face of climate change. 
## 
## Topic 8: Ecology
## V2: 10.1088/1748-9326/ac8c58
## Wildfire activity in the western U.S. has increased in frequency and severity in recent decades. Wildfire smoke emissions contribute to elevated fine particulate matter (PM2.5) concentrations that are dangerous to public health. Due to the outdoor and physically demanding nature of their work, agricultural workers are particularly vulnerable to wildfire smoke pollution. In this study, we quantify the potential exposure of agricultural workers in California to past (2004-2009) and future (2046-2051) smoke PM2.5. We find that while absolute increases in smoke PM2.5 exposure are largest in northern California, agricultural regions in the Central Valley and Central Coast may be highly vulnerable to future increases in smoke PM2.5 concentrations. We find an increase from 6 to 8 million worker smoke exposure days (+35%) of ‘smokewave’ exposure for agricultural workers across the state under future climate conditions, with the largest increases in Tulare, Monterey, and Fresno counties. Under future climate conditions, we find 1.9 million worker smoke exposure days of agricultural worker exposure to levels of total PM2.5 pollution deemed ‘Unhealthy for Sensitive Groups.’ This is a 190% increase over past climate conditions. Wildfire smoke PM2.5 contributes, on average, to more than 90% of these daily PM2.5 exceedances compared with non-fire sources of air pollution. Using the recent extreme wildfire season of 2020 as a case study, we show that existing monitoring networks do not provide adequate sampling of PM2.5 in many future at-risk wildfire regions with large numbers of agricultural workers. Policies will need to consider the changing patterns of smoke PM2.5 exposure under future climate conditions to better protect outdoor agricultural workers. 
## 
## Topic 8: Ecology
## V3: 10.1016/j.jclepro.2021.129953
## Communities are coping with changes in runoff quantity and quality, stemming mainly from changes in climate and land use/land cover (LULC); there is a need to identify the most adaptable strategies that improve community resilience. However, the joint impacts of climate and LULC change have rarely been assessed at local scales. To address these needs, we assessed the response of runoff and pollutant loads from Broad Run, a rapidly developing watershed in northern Virginia, to projected climate and LULC change. Climate data from two downscaled Global Climate Models (GCMs) were used to force an urban watershed model, the Storm Water Management Model (SWMM), while forecasts of LULC change were derived from the Chesapeake Bay Land Change Model (CBLCM). Two Representative Concentration Pathways (RCPs), 4.5 and 8.5, a historic baseline (1995–2020), and projected periods (2040–2065); and four LULC change scenarios designated agricultural conservation (AC), forest conservation (FC), growth management (GM), and historical trend (HT) were used to create a series of ensemble simulations of coupled LULC and climate change. Results indicated that, under RCP 8.5, annual precipitation is projected to increase substantially more than RCP 4.5. Projected LULC change resulted in a projected increase in imperviousness from 6.3% to 13.1%. Results indicated that climate change will likely increase the seasonal variability of runoff, Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) for both RCPs. The largest increase for a single LULC change (without climate change) scenario for runoff, TSS, TN, and TP was 32.6%, 33.4%, 31.6%, and 35.8%, respectively. which occurred with the HT scenario. Results of LULC change also indicated that more pollutant loads were associated with increased imperviousness from increased urban development and loss of deciduous forests and grasslands. The largest increase for climate and LULC change scenarios in runoff, TSS, TN, and TP was 67.6%, 66.7%, 63.4%, and 69.4%, respectively, which occurred with the RCP 8.5 and HT scenarios. Similar, but smaller increases were obtained for other scenarios, suggesting that climate and LULC change may be synergistic, likely undermining watershed restoration efforts. The results of our study also indicate that runoff, TSS, TN, and TP are expected to be more affected by changes in future LULC than by projected changes in climate. Our study can be used to inform watershed restoration efforts, urban planning, and environmental policy. The combined impact of climate and LULC change will likely generate increased runoff, and nutrients and sediment loading, indicating that robust mitigation strategies are needed for watershed restoration to succeed. 
## 
## Topic 8: Ecology
## V4: 10.1088/1748-9326/ac2e37
## Forest disturbances are a critical environmental issue globally and within the boreal biome, yet detailed attribution and trends in disturbances are lacking for many Siberian regions. The Angara region located in the southern taiga of Central Siberia has experienced significant disturbances during the past several decades and is a hotspot of change in Eurasia. Here we estimated fire and logging disturbances using MODIS and Landsat data for the period 2002-2020 across the Angara region and analyzed the resulting trends. Average annual burned and logged area was about 220 and 31 thousand ha or 2 and 0.3% of the study area, respectively. In total, about 4.1 million ha (38% of the region) and 0.6 million ha (6% of the region) were disturbed by fires and logging, respectively. Spatial analysis showed that almost 50% of fires were ignited within 2 km of anthropogenic features such as settlements, roads and logged areas. Almost 5% of the Angara region was burned two or more times during the 19 years of observations. Improved and strictly-enforced conservation and management policies are required to halt continued forest degradation in the Angara region and similarly-affected boreal forests in Siberia. 
## 
## Topic 8: Ecology
## V5: 10.1088/1748-9326/ac97f5
## Anthropogenic sea-level rise poses challenges to coastal areas globally. The combined influence of rising mean sea level (MSL) and storm surges exacerbate the extreme sea level (ESL). Increasing ESL poses a major challenge for climate change adaptation of nearly 2.6 billion inhabitants in the Indian Ocean region. Yet, knowledge about past occurrences of ESL and its progression is limited. Combining multiple tide-gauge and satellite-derived sea-level data, we show that ESL has become more frequent, longer-lasting and intense along the Indian Ocean coastlines. We detect a 2-3-fold increase in ESL occurrence, with higher risk along the Arabian Sea coastline and the Indian Ocean Islands. Our results reveal that rising MSL is the primary contributor to ESL increase (more than 75%), with additional contribution from intensifying tropical cyclones. A two-fold increase in ESL along the Indian Ocean coastline is detected with an additional 0.5 °C warming of the Indian Ocean relative to pre-industrial levels. Utilizing the likely range (17th-83rd percentile as the spread) of Intergovernmental Panel on Climate Change MSL projections with considerable inter-model spread, we show that the Indian Ocean region will be exposed annually to the present-day 100 year ESL event by 2100, irrespective of the greenhouse-gas emission pathways, and by 2050 under the moderate-emission-mitigation-policy scenario. The study provides a robust regional estimate of ESL and its progression with rising MSL, which is important for climate change adaptation policies. 
## 
## Topic 8: Ecology
## V6: 10.1016/j.jclepro.2020.122926
## Particulate matter (PM) has become the main air pollutant in most cities of in China, which greatly threatens the human health. Numerous studies have reported that PM concentrations could be accurately predicted from previous days’ meteorological conditions. However, the hysteretic or lagged effects of meteorological factors on PM concentrations are not clear. In this study, the GeoDetector q statistic method was used to quantify the hysteretic influences of meteorological conditions and their interactions on concentrations of PM2.5 (particles &lt; 2.5 μm in aerodynamic diameter) and PM10 (particles &lt; 10 μm in aerodynamic diameter) and the ratio of PM2.5 to PM10 (PM2.5/PM10) at multiple spatial and temporal scales in China. We found that the temperature, precipitation, and relative humidity had primary impacts on PM2.5 concentrations, PM10 concentrations, and the PM2.5/PM10 ratios, respectively, at both national and annual time scales. At the seasonal time scale, precipitation and relative humidity had hysteretic effects on PM concentrations in spring and summer, respectively. Moreover, precipitation and wind speed were dominant hysteretic factors in autumn and winter, respectively. At the regional scale, the dominant hysteretic effects of meteorological factors showed large variations across all regional subdivisions. The interactions between any two meteorological factors had an enhanced hysteretic effect on PM concentrations compared to a single factor. These findings could help us to develop scientific measures to reduce the emissions of air pollution and improve the performance of air pollution prediction models. 
## 
## Topic 8: Ecology
## V7: 10.1088/1748-9326/acea36
## Climate warming is intensifying the global water cycle, including the rate of fresh water flux between the atmosphere and the surface, determined by precipitation minus evaporation (P−E). Surpluses or deficits of fresh water impact societies and ecosystems, so it is important to monitor and understand how and why P−E patterns and their seasonal range are changing across the globe. Here, annual maximum and minimum P−E and their changes are diagnosed globally over land and ocean using observation-based datasets and CMIP6 climate model experiments covering 1950-2100. Seasonal minimum P−E is negative across much of the globe, apart from the Arctic, mid-latitude oceans and the tropical warm pool. In the global mean, P−E maximum increases and P−E minimum decreases by around 3%-4% per ∘C of global warming from 1995-2014 to 2080-2100 in the ensemble mean of an intermediate greenhouse gas emission scenario. Over land, there is less coherence across the 1960-2020 datasets, but an increase in the seasonal range in P−E emerges in future projections. Patterns of future changes in annual maximum and minimum P−E are qualitatively similar to present day trends with increases in maximum P−E in the equatorial belt and high-latitude regions and decreases in the subtropical subsidence zones. This adds confidence to future projections of a more variable and extreme water cycle but also highlights uncertainties in this response over land. 
## 
## Topic 8: Ecology
## V8: 10.1088/1748-9326/5/2/024008
## Trends towards earlier greenup and increased average greenness have been widely reported in both humid and dry ecosystems. By analyzing NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data from 1982 to 2007, we report complex trends in both the growing season amplitude and seasonally integrated vegetation greenness in southwestern North America and further highlight regions consistently experiencing drought stress. In particular, greenness measurements from 1982 to 2007 show an increasing trend in grasslands but a decreasing trend in shrublands. However, vegetation greenness in this period has experienced a strong cycle, increasing from 1982 to 1993 but decreasing from 1993 to 2007. The significant decrease during the last decade has reduced vegetation greenness by 6% in shrublands and 13% in grasslands (16% and 21%, respectively, in the severe drought years). The greenness cycle correlates to both annual precipitation and dry season length derived from NOAA North America Regional Reanalysis data. If drought events continue as predicted by climate models, they will exacerbate ecosystem degradation and reduce carbon uptake. © 2010 IOP Publishing Ltd. 
## 
## Topic 8: Ecology
## V9: 10.1088/1748-9326/ac1af5
## The Caspian Sea (CS) delivers considerable ecosystem services to millions of people. It experienced water level variations of 3 m during the 20th century alone. Robust scenarios of future CS level are vital to inform environmental risk management and water-use planning. In this study we investigated the water budget variation in the CS drainage basin and its potential impact on CS level during the 21st century using projected climate from selected climate change scenarios of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs), and explored the impact of human extractions. We show that the size of the CS prescribed in climate models determines the modelled water budgets for both historical and future projections. Most future projections show drying over the 21st century. The moisture deficits are more pronounced for extreme radiative forcing scenarios (RCP8.5/SSP585) and for models where a larger CS is prescribed. By 2100, up to 8 (10) m decrease in CS level is found using RCP4.5 (RCP8.5) models, and up to 20 (30) m for SSP245 (SSP585) scenario models. Water extraction rates are as important as climate in controlling future CS level, with potentially up to 7 m further decline, leading to desiccation of the shallow northern CS. This will have wide-ranging implications for the livelihoods of the surrounding communities; increasing vulnerability to freshwater scarcity, transforming ecosystems, as well as impacting the climate system. Caution should be exercised when using individual models to inform policy as projected CS level is so variable between models. We identify that many climate models either ignore, or do not properly prescribe, CS area. No future climate projections include any changes in CS surface area, even when the catchment is projected to be considerably drier. Coupling between atmosphere and lakes within climate models would be a significant advance to capture crucial two-way feedbacks. 
## 
## Topic 8: Ecology
## V10: 10.1088/1748-9326/9/10/104010
## Uncontrolled biomass burning in Indonesia during drought periods damages the landscape, degrades regional air quality, and acts as a disproportionately large source of greenhouse gas emissions. The expansion of forest fires is mostly observed in October in Sumatra favored by persistent droughts during the dry season from June to November. The contribution of anthropogenic warming to the probability of severe droughts is not yet clear. Here, we show evidence that past events in Sumatra were exacerbated by anthropogenic warming and that they will become more frequent under a future emissions scenario. By conducting two sets of atmospheric general circulation model ensemble experiments driven by observed sea surface temperature for 1960-2011, one with and one without an anthropogenic warming component, we found that a recent weakening of the Walker circulation associated with tropical ocean warming increased the probability of severe droughts in Sumatra, despite increasing tropical-mean precipitation. A future increase in the frequency of droughts is then suggested from our analyses of the Coupled Model Intercomparison Project Phase 5 model ensembles. Increasing precipitation to the north of the equator accompanies drier conditions over Indonesia, amplified by enhanced ocean surface warming in the central equatorial Pacific. The resultant precipitation decrease leads to a ∼25% increase in severe drought events from 1951-2000 to 2001-2050. Our results therefore indicate the global warming impact to a potential of wide-spreading forest fires over Indonesia, which requires mitigation policy for disaster prevention. 
## 
## Topic 8: Ecology
## V11: 10.1088/1748-9326/abc5e2
## Understanding past and projected drought patterns across Central America&#39;s &#39;Dry Corridor&#39; (CADC) is crucial for adaptation planning and impact mitigation, especially in small-scale agricultural communities. We analyzed historical and predicted drought patterns in the CADC by calculating Standardized Precipitation Index (SPI) values from local rain gauge records, reanalysis data and a 20-member ensemble of bias-corrected, downscaled CMIP-5 GCMs at both seasonal (3 month) and annual (12 month) scales. Trends in drought frequency, duration, intensity were assessed for three, 30 year future periods compared to historical values. Our results suggest a decrease in mean annual rainfall of 8%-14% in the CADC under moderate to high emissions scenarios, respectively, by end-of-century (2071-2100) relative to a historical baseline (1950-2005). However, projected changes to drought characteristics under these scenarios are more pronounced, with seasonal-scale droughts projected to lengthen by 12%-30%, intensify by 17%-42% and increase in frequency by 21%-24% by end-of-century. Annual-scale, longer-term droughts are projected to lengthen by 68% under moderate emissions, potentially triple in length under high emissions and to intensify by 27%-74%. These results were similar yet slightly more pronounced for some drought metrics when just considering rainy/cropping season months (May-Oct). End-of-century changes to rainfall reliability and drought occurrence such as these would severely impact millions of vulnerable inhabitants in the CADC and should be considered in adaptation policymaking efforts. 
## 
## Topic 8: Ecology
## V12: 10.1088/1748-9326/7/3/035701
## Extreme climatic events like droughts, floods, heat waves and ice storms impact ecosystems as well as human societies. There is wide concern about how terrestrial ecosystems respond to extreme climatic events in the context of global warming. In this study, we used satellite-derived vegetation greenness data (Normalized Difference Vegetation Index; NDVI), insitu weather station data (temperature and precipitation) and the Palmer Drought Severity Index (PDSI) to analyze the spatio-temporal change of the area experiencing vegetation greenness anomalies and extreme climatic events in China from 1982 to 2009. At the national scale, we found that China experienced an increasing trend in heat waves and drought events during the study period. The average fraction of climate stations with drought events (defined by growing season PDSI&lt;2) detected increased from 8% in the 1980s, to nearly 20% in the 2000s, at a rate of 0.6%yr1 (R2=0.61, P&lt;0.001). In contrast, the area showing negative anomalies of vegetation greenness decreased at the rate of 0.9%yr1 from 1982 to 2009 (R2=0.29, P=0.003), although this trend stalled or reversed during the 2000s, particularly in northern China. The decrease in vegetation growth during the last decade over northern China was accompanied by the increase in extreme drought events in the 2000s. In southern China, although both precipitation and PDSI data suggest a greater area experiencing drought events during the 2000s than in the 1980s, the area showing negative vegetation greenness decreased consistently during the whole study period. © 2012 IOP Publishing Ltd. 
## 
## Topic 8: Ecology
## V13: 10.1088/1748-9326/9/6/064002
## Climate extremes have and will continue to cause severe damages to buildings and natural environments around the world. A full knowledge of the probability of the climate extremes is important for the management and mitigation of natural hazards. Based on Mann-Kendall trend test and copulas, this study investigated the characteristics of precipitation extremes as well as their implications in southwestern China (Yunnan, Guangxi and Guizhou Province), through analyzing the changing trends and probabilistic characteristics of six indices, including the consecutive dry days, consecutive wet days, annual total wet day precipitation, heavy precipitation days (R25), max 5 day precipitation amount (Rx5) and the rainy days (RDs). Results showed that the study area had generally become drier (regional mean annual precipitation decreased by 11.4 mm per decade) and experienced enhanced precipitation extremes in the past 60 years. Relatively higher risk of drought in Yuanan and flood in Guangxi was observed, respectively. However, the changing trends of the precipitation extremes were not spatially uniform: increasing risk of extreme wet events for Guangxi and Guizhou, and increasing probability of concurrent extreme wet and dry events for Yunnan. Meanwhile, trend analyses of the 10 year return levels of the selected indices implied that the severity of droughts decreased in Yunnan but increased significantly in Guangxi and Guizhou, and the severity of floods increased in Yunnan and Guangxi in the past decades. Hence, the policy-makers need to be aware of the different characterizations and the spatial heterogeneity of the precipitation extremes. © 2014 IOP Publishing Ltd. 
## 
## Topic 8: Ecology
## V14: 10.1088/1748-9326/ac31ec
## Northern China experienced two intense dust storms in March 2021, leading to reduced visibility and excessive particulate pollution. Understanding the cause of such extreme phenomena is important for further prevention. This study successfully reproduced the extreme dust storms using the Community Multiscale Air Quality model with refined bulk density of different soil types and improved spatial resolution. The wind-blown PM2.5 and PM10 are estimated to be around 15 and 120 µg m−3 in dust source areas (equal 9.6% and 31.0% in average of China), resulting in 1.1 and 2.0 times increases in PM2.5 and PM10 concentrations in populated regions of the Middle Yellow River Basin and the Beijing-Tianjin-Hebei area. The critical threshold friction velocity is the key parameter to judge whether wind-blown dust occurs. Dust flux is sensitive to the bulk soil density (increased by 4.2% and 12.6% for PM2.5 and PM10 after refined soil bulk density) and resolution (increased by 13.5% and 3.5% for PM2.5 and PM10 from 27 km to 9 km). Such results demonstrated the strong correlation between wind speed, frequency, and intensity of dust phenomena from 2013 to 2021. The wind speed can be further enhanced in dust source areas even in the context of a decline in the national average, leading to more frequent and persistent dust storms in March 2050. Only relying on coordinated emission reductions to mitigate climate change, wind-blown dust in northern China still poses considerable potential risks to air quality. Urgent actions should also be taken to improve land-use and land-cover to reduce the area of dust sources. 
## 
## Topic 8: Ecology
## V15: 10.1088/1748-9326/ab9141
## Understanding how climate change and demographic factors may shape future population exposure to viruses such as Zika, dengue, or chikungunya, transmitted by Aedes mosquitoes is essential to improving public health preparedness. In this study, we combine projections of cumulative monthly Aedes-borne virus transmission risk with spatially explicit population projections for vulnerable demographic groups to explore future county-level population exposure across the conterminous United States. We employ a scenario matrix - combinations of climate scenarios (Representative Concentration Pathways) and socioeconomic scenarios (Shared Socioeconomic Pathways) - to assess the full range of uncertainty in emissions, socioeconomic development, and demographic change. Human exposure is projected to increase under most scenarios, up to + 177% at the national scale in 2080 under SSP5∗RCP8.5 relative to a historical baseline. Projected exposure changes are predominantly driven by population changes in vulnerable demographic groups, although climate change is also important, particularly in the western region where future exposure would be about 30% lower under RCP2.6 compared to RCP8.5. The results emphasize the crucial role that socioeconomic and demographic change play in shaping future population vulnerability and exposure to Aedes-borne virus transmission risk in the United States, and underline the importance of including socioeconomic scenarios in projections of climate-related vector-borne disease impacts. 
## 
## Topic 8: Ecology
## V16: 10.1088/1748-9326/8/4/045025
## Increases in air, permafrost, and sea surface temperature, loss of sea ice, the potential for increased wave energy, and higher river discharge may all be interacting to escalate erosion of arctic coastal lowland landscapes. Here we use airborne light detection and ranging (LiDAR) data acquired in 2006 and 2010 to detect landscape change in a 100 km2 study area on the Beaufort Sea coastal plain of northern Alaska. We detected statistically significant change (99% confidence interval), defined as contiguous areas (&gt;10 m 2) that had changed in height by at least 0.55 m, in 0.3% of the study region. Erosional features indicative of ice-rich permafrost degradation were associated with ice-bonded coastal, river, and lake bluffs, frost mounds, ice wedges, and thermo-erosional gullies. These features accounted for about half of the area where vertical change was detected. Inferred thermo-denudation and thermo-abrasion of coastal and river bluffs likely accounted for the dominant permafrost-related degradational processes with respect to area (42%) and volume (51%). More than 300 thermokarst pits significantly subsided during the study period, likely as a result of storm surge flooding of low-lying tundra (&lt;1.4 m asl) as well as the lasting impact of warm summers in the late-1980s and mid-1990s. Our results indicate that repeat airborne LiDAR can be used to detect landscape change in arctic coastal lowland regions at large spatial scales over sub-decadal time periods. © 2013 IOP Publishing Ltd. 
## 
## Topic 8: Ecology
## V17: 10.1088/1748-9326/aade09
## Anthropogenic climate change is altering ecological and human systems globally, including in United States (US) national parks, which conserve unique biodiversity and resources. Yet, the magnitude and spatial patterns of climate change across all the parks have been unknown. Here, in the first spatial analysis of historical and projected temperature and precipitation across all 417 US national parks, we show that climate change exposes the national park area more than the US as a whole. This occurs because extensive parts of the national park area are in the Arctic, at high elevations, or in the arid southwestern US. Between 1895 and 2010, mean annual temperature of the national park area increased 1.0 °C ± 0.2 °C century-1 (mean ± standard error), double the US rate. Temperature has increased most in Alaska and its extensive national parks. Annual precipitation of the national park area declined significantly on 12% of national park area, compared to 3% of the US. Higher temperatures due to climate change have coincided with low precipitation in the southwestern US, intensifying droughts in the region. Physical and ecological changes have been detected and attributed mainly to anthropogenic climate change in areas of significant temperature increases in US national parks. From 2000 to 2100, under the highest emissions scenario (representative concentration pathway [RCP] 8.5), park temperatures would increase 3 °C-9 °C, with climate velocities outpacing dispersal capabilities of many plant and animal species. Even under the scenario of reduced emissions (RCP2.6), temperature increases could exceed 2 °C for 58% of national park area, compared to 22% of the US. Nevertheless, greenhouse gas emissions reductions could reduce projected temperature increases in national parks by one-half to two-thirds. 
## 
## Topic 8: Ecology
## V18: 10.1016/j.jclepro.2020.122249
## Studying the trends and ecological impacts of basin land degradation is important to protect lake and reservoir water quality and for basin land-use planning. This study examined 12 lakes/reservoirs and their basins in China, and based on basin land-use and lake/reservoir water-quality indicator data (pH, dissolved oxygen (DO), permanganate index (CODMn), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP)) from 2005, 2010, and 2015, the land degradation trends of the basins and their relationships to lake/reservoir water quality were analyzed using Spearman correlation analysis, redundancy analysis (RDA), and geographic information system (GIS) techniques. The results showed that: (1) The proportion of ecological land (change rate: −0.06%) and cultivated land (change rate: −0.38%) showed a downward trend, and the proportion of degraded land showed a significant upward trend (change rate: +2.90%). The increase in built-up land was the fastest among all land types (change rate: +2.93%). (2) The proportion of degraded land (dominated by built-up land) and cultivated land (dominated by cropland) in the basins was significantly positively correlated with CODMn, NH3-N, TN, and TP but significantly negatively correlated with DO. Conversely, the proportion of ecological land (dominated by forest land) in the basins was significantly negatively correlated with CODMn, NH3-N, TN, and TP but significantly positively correlated with DO. This showed that basin land degradation had a significant adverse impact on lake/reservoir water quality. (3) RDA indicated that basin land degradation explained up to 58.6% of lake/reservoir water-quality changes. Degraded land had the highest RDA explanation rate (31.4%) for lake/reservoir water-quality changes, and within this category, built-up land had the highest explanation rate (built-up land: 27.9%; desertified land: 12.7%). Ecological land explained 29.2% of lake/reservoir water-quality changes, within which forest land had the highest explanation rate (forest land: 24.7%; grassland: 9.4%; wetland: 12.5%). Cultivated land explained 22.7% of lake/reservoir water-quality changes, within which cropland had the highest explanation rate (cropland: 19.7%; garden land: 4.9%). In conclusion, the land in the selected lake/reservoir basins showed a clear trend of degradation, and this degradation trend had a significant adverse impact on the changes in lake/reservoir water quality. The results of this study can support policymakers to contain basin land degradation, to protect lake/reservoir water quality and to improve the sustainability of basin land and water resources. 
## 
## Topic 8: Ecology
## V19: 10.1088/1748-9326/7/4/044034
## Stagnant atmospheric conditions can lead to hazardous air quality by allowing ozone and particulate matter to accumulate and persist in the near-surface environment. By changing atmospheric circulation and precipitation patterns, global warming could alter the meteorological factors that regulate air stagnation frequency. We analyze the response of the National Climatic Data Center (NCDC) air stagnation index (ASI) to anthropogenically enhanced radiative forcing using global climate model projections of late-21st century climate change (SRESA1B scenario). Our results indicate that the atmospheric conditions over the highly populated, highly industrialized regions of the eastern United States, Mediterranean Europe, and eastern China are particularly sensitive to global warming, with the occurrence of stagnant conditions projected to increase by 12-25% relative to late-20th century stagnation frequencies (3-18 + days yr-1). Changes in the position/strength of the polar jet, in the occurrence of light surface winds, and in the number of precipitation-free days all contribute to more frequent late-21st century air mass stagnation over these high-population regions. In addition, we find substantial inter-model spread in the simulated response of stagnation conditions over some regions using either native or bias corrected global climate model simulations, suggesting that changes in the atmospheric circulation and/or the distribution of precipitation represent important sources of uncertainty in the response of air quality to global warming. © 2012 IOP Publishing Ltd. 
## 
## Topic 8: Ecology
## V20: 10.1088/1748-9326/ac66f3
## City-level estimates of ambient ozone concentrations and associated disease burdens are sparsely available, especially for low and middle-income countries. Recently available high-resolution gridded global ozone concentration estimates allow for estimating ozone concentrations and mortality at urban scales and for urban-rural catchment areas worldwide. We applied existing fine resolution global surface ozone estimates, developed by integrating observations (8834 sites globally) with nine atmospheric chemistry models, in an epidemiologically-derived health impact function to estimate chronic respiratory disease mortality worldwide in 2019. We compared ozone season daily maximum 8 h mixing ratio concentrations and ozone-attributable mortality for urban areas worldwide (including cities and densely-populated towns), and their surrounding peri-urban, peri-rural, and rural areas. In 2019, population-weighted mean ozone among all urban-rural catchment areas was greatest in peri-urban areas (52 ppb), followed by urban areas (cities and towns; 49 ppb). Of 423 100 estimated global ozone-attributable deaths, 37% (147 100) occurred in urban areas, where 40% of the world&#39;s population resides, and 56% (254 000) occurred in peri-urban areas (&lt;1 h from an urban area), where 47% of the world&#39;s population resides. Across 12 946 cities (excluding towns), average population-weighted mean ozone was 51 ppb (sd = 13 ppb, range = 10-78 ppb). Three quarters of the ozone-attributable deaths worldwide (77%; 112 700) occurred in cities of South and East Asia. City-level ozone-attributable mortality rates varied by a factor of 10 across world regions. Ozone levels and attributable mortality were greatest in Asian and African cities; however, cities of higher-income regions, like high-income Asia Pacific and North America, continue to experience high ozone concentrations and attributable mortality rates, despite successful national air quality measures for reducing ozone precursor emissions. The disproportionate magnitude of ozone mortality compared with population size in peri-urban areas indicates that reducing ozone precursor emissions in places that influence peri-urban concentrations can yield substantial health benefits in these areas. 
## 
## Topic 8: Ecology
## V21: 10.1088/1748-9326/8/2/024026
## The relationship between the frequency of spring dust storms over Tarim Basin in northwest China and the winter North Atlantic Oscillation (NAO) is investigated by using the observed dust storm frequency (DSF) and the 10 m wind velocity at 36 stations in Tarim Basin and the National Centers for Environment Prediction/National Center for Atmospheric Research reanalysis data for the period 1961-2007. The spring DSF (winter NAO) index shows a clear decreasing (increasing) linear trend over 1961-2007. The winter NAO correlates well with the subsequent spring DSF over Tarim Basin on both interannual and interdecadal time scales and its interannual to interdecadal variation plays an important role in the spring DSF. Two possible physical mechanisms are identified. One is related to the large scale anomalous circulations in spring in the middle to high troposphere modulated by the winter NAO, providing the background of dynamical conditions for the dust storm occurrences. The other is related to the shifts in the local horizontal sea level pressure (SLP) gradients and 10 m wind speed, corresponding to changes in the large scale circulations in spring. The decrease in the local 10 m wind speed due to the reduced horizontal SLP gradients over Tarim Basin during the strong winter NAO years contributes to the decline of the DSF in the subsequent spring. © 2013 IOP Publishing Ltd. 
## 
## Topic 8: Ecology
## V22: 10.1088/1748-9326/aba20c
## Anthropogenic aerosol emissions are predicted to decline sharply throughout the 21st century, in line with climate change and air quality mitigation policies, causing a near-term warming of climate that will impact our trajectory towards 1.5 °C above pre-industrial temperatures. However, the persistent uncertainty in aerosol radiative forcing limits our understanding of how much the global mean temperature will respond to near-term reductions in anthropogenic aerosol emissions. We quantify the model and scenario uncertainty in global mean aerosol radiative forcing up to 2050 using statistical emulation of a perturbed parameter ensemble for emission reduction scenarios consistent with three Shared Socioeconomic Pathways. We then use a simple climate model to translate the uncertainty in aerosol radiative forcing into uncertainty in global mean temperature projections, accounting additionally for the potential correlation of aerosol radiative forcing and climate sensitivity. Near-term aerosol radiative forcing uncertainty alone causes an uncertainty window of around 5 years (2034-2039) on the projected year of exceeding a global temperature rise of 1.5 °C above pre-industrial temperatures for a middle of the road emissions scenario (SSP2-RCP4.5). A correlation between aerosol radiative forcing and climate sensitivity would increase the 1.5 °C exceedance window by many years. The results highlight the importance of quantifying aerosol radiative forcing and any relationship with climate sensitivity in climate models in order to reduce uncertainty in temperature projections. 
## 
## Topic 8: Ecology
## V23: 10.1088/1748-9326/aba926
## High Mountain Asia (HMA), which includes the Tibetan Plateau, Tienshan Mountains and surrounding region, has abundant snowfall and a long period of snow cover annually. The headwaters of many prominent Asian rivers depend in part on HMA meltwater. In this study, we evaluate projected changes in mean snowfall (Smean), snowfall days (Sd), and snowfall fraction (Sf) for the years 2070-2099 relative to 1976-2005, under the Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) emission scenarios. An evaluation of the results shows that while NASA&#39;s NEX-GDDP (National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections) high-resolution daily downscaled dataset can successfully capture the distribution of mean snowfall climatology, it has a strong bias for extreme snowfall indices. In general, the projected increase of temperature under RCP4.5 and RCP8.5-especially in winter-will result in a decrease in snowfall amount (-18.9%,-32.8%), fewer snowfall days (-29.6%,-47.3%), and less precipitation falling as snow (-26.7%,-42.3%). Furthermore, under high emission scenarios, rain-dominated regions are projected to expand 53.9%, while snow-dominated areas will only account for 17.9% of the entire HMA. Spatially, snowfall shows a more robust decline in eastern HMA (e.g. East Tienshan, East Kun Lun, Qilian, South and Eastern Tibet, and Hengduan) than in western HMA (e.g. Hissar Alay, Pamir, and Karakoram). This difference can be attributed to various environmental factors, such as climatology, elevation influences, and the unique seasonal recycle between the two regions. 
## 
## Topic 8: Ecology
## V24: 10.1016/j.jclepro.2022.133148
## The water environment has experienced prominent changes worldwide in recent decades, especially in inland waters. As an important lake region in China, the water turbidity of lakes/reservoirs in the Northeast Plain-Mountainous Region (NPLR) remains less understood, especially the influencing mechanisms of regional climatic conditions and human activities. To enhance the knowledge of turbidity dynamics and explanatory variables of NPLR water bodies, long-term turbidity estimates of lakes/reservoirs were derived from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua observations between 2003 and 2019. The results show that most lakes/reservoirs became less turbid in NPLR during the study period, as indicated by a decreasing turbidity trend of 0.05–2.4 nephelometric turbidity units (NTU)/year. Overall, the decreased turbidity was mainly attributed to the decreased wind speed (from 2003 to 2012, r = 0.85, p &lt; 0.01), but also to the increased normalized difference vegetation index (NDVI) of the forest region (r = −0.80, p &lt; 0.01), with the latter being a result of the forest protection efforts of the Chinese government. The increased snowmelt depth also contributed to a substantial turbidity decline in April. In addition, distinct seasonal patterns of turbidity were observed in the NPLR, with less turbid water in the wet season (i.e., from June to August) and turbid water in the dry season (i.e., October, November, and April in the next year). This result is attributed to the rapid seasonal dynamics of precipitation and runoff. This study provided a comprehensive analysis on the influencing mechanisms of turbidity dynamics in NPLR lakes; in particular, for the first time, revealed the impacts of the national policy-mandated forest development and the snowmelt depth on lake turbidity. These findings will provide vital information for government decision-making in water environment protection in NPLR and other similar areas with densely distributed lakes. 
## 
## Topic 8: Ecology
## V25: 10.1088/1748-9326/ac109c
## Aerosol concentrations over Asia play a key role in modulating the Indian summer monsoon (ISM) rainfall. Lockdown measures imposed to prevent the spread of the COVID-19 pandemic led to substantial reductions in observed Asian aerosol loadings. Here, we use bottom-up estimates of anthropogenic emissions based on national mobility data from Google and Apple, along with simulations from the ECHAM6-HAMMOZ state-of-the-art aerosol-chemistry-climate model to investigate the impact of the reduced aerosol and gases pollution loadings on the ISM. We show that the decrease in anthropogenic emissions led to a 4 W m-2 increase in surface solar radiation over parts of South Asia, which resulted in a strengthening of the ISM. Simultaneously, while natural emission parameterizations are kept the same in all our simulations, the anthropogenic emission reduction led to changes in the atmospheric circulation, causing accumulation of dust over the Tibetan plateau (TP) during the pre-monsoon and monsoon seasons. This accumulated dust has intensified the warm core over the TP that reinforced the intensification of the Hadley circulation. The associated cross-equatorial moisture influx over the Indian landmass led to an enhanced amount of rainfall by 4% (0.2 mm d-1) over the Indian landmass and 5%-15% (0.8-3 mm d-1) over central India. These estimates may vary under the influence of large-scale coupled atmosphere-ocean oscillations (e.g. El Nino Southern Oscillation, Indian Ocean Dipole). Our study indicates that the reduced anthropogenic emissions caused by the unprecedented COVID-19 restrictions had a favourable effect on the hydrological cycle over South Asia, which has been facing water scarcity during the past decades. This emphasizes the need for stringent measures to limit future anthropogenic emissions in South Asia for protecting one of the world&#39;s most densely populated regions. 
## 
## Topic 9: Energy Systems
## V1: 10.1016/j.egyr.2023.06.030
## One of the crucial issues in the expansion of isolated wind energy conversion systems (WECSs) is the design of a grid forming control structure that is robust to changes of the system&#39;s operating point in the steady-state as well as transient conditions. This paper has addressed the voltage/frequency control of a local microgrid as well as flux and slip control of a squirrel cage induction generator that feeds the microgrid via a back-to-back converter in an isolated WECS. An adaptive nonlinear voltage control structure has been proposed for load side converter (LSC) consists of a constant frequency voltage regulator and a current controller based on the Lyapunov function method (LFM), which are connected in cascade. In an outer loop, the reference current of LSC is generated by the voltage regulator designated for maintaining the voltage/frequency of the microgrid. In an inner loop, the reference signals are robustly tracked by the current controller. In a novel reference frame that its angle difference with the stationary reference frame is determined by a dual loop DC link voltage controller, an LFM-based nonlinear flux controller is proposed for the machine side converter (MSC). Using the proposed flux control structure, in addition to maintaining the flux magnitude at a nominal value, by controlling the generator slip through adjusting the speed of the proposed reference frame, the power balance is established and the DC voltage is maintained at a pre-set value. The performance of the proposed controllers has been investigated through simulation in the MATLAB® software. The obtained results confirm the robustness and stability of the proposed control structure with respect to various disturbances and uncertainties. 
## 
## Topic 9: Energy Systems
## V2: 10.1016/j.segan.2023.101086
## With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. This paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments. 
## 
## Topic 9: Energy Systems
## V3: 10.1109/TSTE.2019.2897549
## This paper presents the design, modeling, and optimal power generation control of a large hybrid wind turbine transmission system that seamlessly integrates planetary/parallel gear sets with a hydraulic transmission to improve the turbine&#39;s reliability and efficiency. The hybrid wind turbine has power splitting flows including both mechanical and hydraulic power transmissions. The turbine transmission ratio can be controlled to continuously vary for the maximum wind power extraction and grid integration. Dynamics of the hybrid wind turbine is modeled as an incremental disturbed state space model based on the dynamic equations of each mechanical/hydraulic element. To achieve good tracking and robustness performance, an optimal H∞ loop-shaping pressure controller is designed, which accurately tracks the optimal load pressure in the hydraulic transmission for maximizing wind power generations. The validations of the proposed hybrid wind turbine and the H∞ loop-shaping pressure controller are performed based on a detailed aero-hydro-servo-elastic hybrid type wind turbine simulation platform with both mechanical geared transmission and hydraulic transmission, which is adapted from the National Renewable Energy Laboratory 5 MW monopile wind turbine model within fatigue, aerodynamics, structures, and turbulence code. The validation results demonstrate that the hybrid wind turbine achieves better performance in both the maximum wind power extraction and power quality than the hydrostatic wind turbine. In addition, the proposed H∞ loop-shaping pressure controller has better tracking performance than the traditional proportional integral controller. 
## 
## Topic 9: Energy Systems
## V4: 10.1016/j.apenergy.2021.118488
## Mobile energy storage systems (MESSs) are becoming crucial devices to maintain stable power distribution system operations under the operation of voltage regulators including on-load tap changers, capacitor banks, and smart inverters of photovoltaic (PV) systems. This study proposes a joint framework in which the operation of voltage regulators and the road routing of MESSs are co-optimized for Volt/VAR control (VVC) in both power distribution and transportation networks. The proposed framework aims to minimize real power loss and voltage deviation along with peak shaving in power distribution networks, and to reduce the MESS traveling cost in transportation networks. Under the uncertainty of the predicted error of the PV generation output, the proposed framework is formulated as a chance-constrained optimization problem using mixed-integer linear programming. The proposed joint optimization algorithm was simulated in the IEEE 13-bus and IEEE 33-bus systems, which were coupled with 9-node and 15-node transportation systems, respectively. The results show that, in contrast with a VVC method without MESS, the proposed algorithm using MESS reduces the total real power loss and peak load by 9.7% and 15.92%, respectively, in the IEEE 13-bus system, and 2.55% and 3.47%, respectively, in the IEEE 33-bus system. 
## 
## Topic 9: Energy Systems
## V5: 10.1016/j.egyr.2022.08.091
## Traditional on-load tap changer (OLTC) has problems such as easy oscillation and low regulation precision in the voltage regulation control of the power system. In order to seek a speedy and stable voltage control strategy, a flexible on-load voltage regulator is studied in this paper. A stepless energy take-off winding is set on the high-voltage side to connect the back-to-back converter, and the winding side of the converter is used to realize reactive power compensation. The grid side of the converter can realize continuous voltage regulation. A coordinated voltage regulation control strategy between flexible on-load voltage regulator and static synchronous compensator (STATCOM) is proposed. The tap positions of the flexible on-load voltage regulator and the reactive power sent by the STATCOM are used as reference signals to ensure sufficient capacity of the compensator and reduce the number of tap actions. The strategy proposed in this paper is compared with the voltage regulation method based on the traditional voltage regulation devices, the effectiveness of the strategy is demonstrated by some simulations based on Matlab/Simulink. 
## 
## Topic 9: Energy Systems
## V6: 10.1016/j.ijepes.2018.03.022
## In this paper a new nonlinear regulation for synchronous generator excitation control is presented. This method comprises the nonlinear Interconnection and Damping Assignment (IDA) method together with the generator voltage feedback giving a nonlinear Automatic Voltage Regulator (AVR). First, the IDA excitation control is introduced in the third order salient pole synchronous generator mathematical model represented as a Port Controlled Hamiltonian (PCH) system. Thereafter, a generator voltage feedback signal is added. It is proven that adding the feedback to IDA control preserves a PCH structure of a system and the system is therefore passive. In the paper it is also proven that the gained system is stable and that the reference operating point corresponds to the minimal energy point of the system. Finally, the gained IDA AVR is tested with seventh order synchronous generator model and compared to classical generator excitation controllers. The simulations show the efficient tracking of generator voltage reference value, as well as the maintenance of stability in case of grid-side short-circuit occurrence. The main advantages of the proposed controller are efficiently generator voltage tracking and attenuation of electromechanical oscillations in case of a large disturbance, such as grid-side short-circuit. 
## 
## Topic 9: Energy Systems
## V7: 10.1016/j.ijepes.2019.105517
## This paper proposes a conditional value-at-risk (CVaR) based two-stage model predictive control (MPC) method for efficient dynamic load restoration decision-making in the coupled transmission and distribution (TS-DS) system with renewable energy. The CVaR values are employed to describe uncertainties of the load and source sides. It benefits on-line load restoration with uncertainties by fast uncertainty management and prediction error correction. In order to improve the computation of the multi-step load restoration optimization in the coupled TS-DS system, a two-stage load restoration model is constructed with the first stage relaxed multi-step optimization and the second stage single-step tracing optimization. By solving linear programming (LP), mixed integer linear programming (MILP) and mixed integer quadratic programming (MIQP) problems, the proposed CVaR based two-stage MPC method achieves on-line receding horizon load restoration of the coupled TS-DS system facing with load-source uncertainty. The effectiveness of the proposed method is validated using the IEEE-118 and IEEE-33 test systems, and a real-world coupled TS-DS system. 
## 
## Topic 9: Energy Systems
## V8: 10.1016/j.ijepes.2023.109158
## The model-based voltage control is widely used to mitigate quick voltage fluctuations caused by renewable energy uncertainties. However, the accurate and complete parameters of the distribution system are rarely available in practice. A two-stage voltage regulation framework based on a mixed-integer second order cone optimization programming (MISOCP) model and graph convolutional network-based deep reinforcement learning (GCN-DRL) is proposed for active distribution system voltage regulation. Specifically, in the day-ahead stage, a MISOCP is proposed for hourly voltage regulation optimization with capacitor banks (CBs), on-load tap changers (OLTC), and energy storage systems (ESS). Then, a GCN-DRL method is proposed in the real-time stage for dispatching reactive power from the intelligent inverters connected to the photovoltaic systems to alleviate the voltage fluctuations. The proposed grid topological graph convolutional network (GTGCN) leverages the distribution system&#39;s graph structure information and the convolutional operation to capture and embed the graphical features among nodal measurements. Then, the deep deterministic policy gradient (DDPG) is innovatively proposed for GCN-DRL to learn the high-efficiency voltage regulation policies, which can be implemented in a real-time manner in practice. The proposed voltage regulation model is investigated on a modified IEEE 33-node distribution system and a 25-node unbalanced distribution system. The numerical results illustrate the high effectiveness and efficiency of the proposed adaptive robust operating model. 
## 
## Topic 9: Energy Systems
## V9: 10.17775/CSEEJPES.2020.00600
## Reactive power sharing cannot be achieved using many existing microgrid (MG) control methods, but the convergence speed of these methods is slow. To solve these problems, a finite-time distributed control approach is proposed in this paper, which is based on the hierarchical control structure. The hierarchical control structure consists of a dual loop control, a droop control used as a primary control and a secondary control. First, the secondary controller is modeled, and the MG system composed of distributed generators (DGs) is considered as a multi-agent system. The secondary controller consists of a frequency regulator, voltage regulator and power regulator. Secondly, the adaptive virtual impedance module is established, using the output of the reactive power regulator as its input. Thirdly, a dual loop controller is combined with a primary controller and secondary controller to generate a pulse width modulation (PWM) signal to control the power and voltage of the MG. In order to reduce the fluctuation of the MG, a damping module is introduced when the structure of the system changes. Finally, the stability of the proposed control strategy is proved by the related theorems. A simulation system is established in the Matlab environment, and the simulation results show that the proposed method is effective. 
## 
## Topic 9: Energy Systems
## V10: 10.1109/JESTPE.2021.3137397
## The battery energy stored quasi-Z source inverter (BES-qZSI)-based photovoltaic (PV) power system combines the advantages of the qZSI and energy storage system. However, as the BES-qZSI is a fast-response power converter without any inertia, when applied as a grid-connected system, it leads to decreased power system inertia. The low inertia problem will degrade the system&#39;s performance and affect the system&#39;s stability. Moreover, the BES-qZSI PV power system usually employs a phase-locked loop and PR regulator to control the grid current to track the desired reference in phase with the grid voltage. These do not reflect the characteristics of frequency modulation and voltage regulation. To enhance the system&#39;s frequency and voltage self-adjustment ability, a control strategy based on the virtual synchronous generator (VSG) technique for the BES-qZSI PV power system is proposed in this article. By introducing the concept of synchronous generator, the BES-qZSI PV power system has the characteristics of the heavy moment of inertia, droop frequency active, droop characteristic of voltage-reactive power, and so on. Then, an active power control strategy integrated with reactive power compensation is proposed to implement PV generation and reactive power compensation at the same time. The simulation and experimental results verify the proposed control strategy. 
## 
## Topic 9: Energy Systems
## V11: 10.1016/j.enconman.2020.113521
## In order to accurately and efficiently evaluate the actual operating status of photovoltaic (PV) power stations, this study proposes a novel design of quick current–voltage (I-V) curve tracer with automatic parameter extraction for PV arrays. The I-V tracer exploits the dynamic capacitor charging to quickly and safely scan the output I-V curve of PV array. Particularly, an adaptive sampling interval and charging and discharging time estimation method is proposed to achieve the uniform sampling of the I-V curve for improving the data quality. Moreover, an efficient grid search and improved Nelder-Mead simplex (GS-INMS) based hybrid optimization algorithm is proposed for PV model parameters identification. In the GS-INMS algorithm, the grid search method is used for finding starting point for the NMS optimization algorithm, which can improve the global search capability. Meanwhile, the basic NMS algorithm is modified to improve the local convergence. The performance of the proposed algorithm is verified and compared with some state-of-the-art methods on several experimental datasets, including two benchmark I-V curves, large dataset of I-V curves from the National Renewable Energy Laboratory (NREL), and the I-V curves collected by the designed I-V tracer system. Comprehensive comparison results demonstrate that the proposed GS-INMS shows obvious superiority in accuracy, convergence and robustness. Meanwhile, because of low complexity, this algorithm is embedded in the designed tracer for real-time parameters extraction of the single-diode model of PV arrays. In addition, the experimental results of field testing on a small-scale PV array indicate that the I-V tracer can quickly acquire I-V curves of high quality and perform accurate model parameter identification in real time. 
## 
## Topic 9: Energy Systems
## V12: 10.1109/TPWRS.2021.3102870
## A distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution. 
## 
## Topic 9: Energy Systems
## V13: 10.35833/MPCE.2022.000146
## High penetration of distributed renewable energy sources and electric vehicles (EVs) makes future active distribution network (ADN) highly variable. These characteristics put great challenges to traditional voltage control methods. Voltage control based on the deep Q-network (DQN) algorithm offers a potential solution to this problem because it possesses human-level control performance. However, the traditional DQN methods may produce overestimation of action reward values, resulting in degradation of obtained solutions. In this paper, an intelligent voltage control method based on averaged weighted double deep Q-network (AWDDQN) algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network (DDQN) algorithm. Using the proposed method, the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process (MDP) model which is solved by the AWDDQN algorithm. The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN. The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs, and compared with the DQN and DDQN algorithms based methods, and traditional mixed-integer nonlinear program based methods. The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones. 
## 
## Topic 9: Energy Systems
## V14: 10.1016/j.ijepes.2016.02.022
## The paper presents the bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) algorithm based robust controllers for voltage deviations due to the variation of reactive power in an isolated wind-diesel hybrid power system. The isolated wind-diesel system consists of wind energy conversion system (WECS) utilizing a permanent magnet induction generator (PMIG). Further, a synchronous generator (SG) is used with the diesel engine set for power generation. The mismatch between generated and consumed reactive power in the system causes voltage fluctuations, which will occur at generator terminals. These oscillations further causes reduction in the stability and quality of the power supply. The static synchronous compensator (STATCOM) and an automatic voltage regulator (AVR) are used to suppress voltage fluctuations in an isolated wind-diesel hybrid power system. The STATCOM is used as a reactive power compensator and the AVR is used to keep the terminal voltage constant for the synchronous generator. Both STATCOM and AVR are having proportional and integral (PI) controllers with single input. In modeling for the system, a normalized co-prime factorization is applied to show the possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. The performance and robust stability conditions of the control system are formulated as the optimization problem, which is based on the Hα loop shaping. BFOA and PSO algorithms are implemented to solve this optimization problem and to achieve PI control parameters of STATCOM and AVR simultaneously. In order to show the efficiency of the proposed controllers, the performance of the proposed controllers is compared with the performance of the conventional controller and genetic algorithm (GA) based PI controllers for the same wind-diesel system. The dynamic responses of the system for four different small-disturbance case studies has been carried out in MATLAB environment. 
## 
## Topic 9: Energy Systems
## V15: 10.1109/TPWRS.2018.2869195
## Smart inverters provide additional control capability to help optimize the operation of distribution systems. This paper proposes a framework for exact optimal active and reactive power dispatch of distributed photovoltaic (PV) generation, switched capacitors, and voltage regulators in large multi-phase unbalanced distribution systems. The objectives of the optimal dispatch are minimization of the energy loss, PV active power curtailment, and operations of capacitors and voltage regulators, in addition to elimination of voltage violations and reverse power flow. The optimization problem is formulated in rectangular coordinates as a nonlinear, nonconvex problem. Effective computational strategies are proposed to allow the application of predictor-corrector primal-dual interior point method to solve optimization problems in real-time with a large number of constraints and variables, including discrete variables corresponding to switched capacitors and voltage regulators. The accuracy of the numerical solution and the ability to implement the proposed framework are validated using the unbalanced multi-phase IEEE 34-bus and EPRI 2,998-bus distribution systems with 15-minute load and PV data. The results show a significant loss reduction and elimination of both voltage violations and reverse power flow. 
## 
## Topic 9: Energy Systems
## V16: 10.1016/j.energy.2019.115861
## In this paper, a fractional nonlinear synergetic controller is developed for efficient power extraction and operation of a variable speed wind energy conversion system. The proposed fractional nonlinear synergetic control scheme includes: (1) rotational speed regulation for maximum power extraction at the generator converter side through the DC-DC boost converter; (2) DC link voltage regulation and active-reactive power regulation, by controlling the direct-quadratic components of the current, for power transfer at unity power factor, at the grid side converter through the inverter. Analytical design of aggregated regulator has been used in the nonlinear synergetic control design with the introduction of a fractional macro-variable in order to increase the degree of freedom of the classical synergetic controller. The stability of the closed loop system using the macro-variable is guaranteed and a fast convergence can be reached through the design parameter. Experimentation, on a permanent magnet synchronous generator based wind turbine setup, is conducted to validate the proposed fractional nonlinear synergetic control system for operating the generator and the grid sides of the power system. Different cases are studied and the experimental results have shown that the system controlled by the proposed control method is stable and tracks perfectly the desired reference. 
## 
## Topic 9: Energy Systems
## V17: 10.1002/we.1695
## A new inverse design process for horizontal axis wind turbine blades is developed to account for three-dimensional blade features such as non-planar wing tip. The multidimensional Newton iteration method combined with a vortex line method is used to provide blade geometry parameters given desired aerodynamic behaviors such as lift coefficient and axial induction. The Jacobian matrix is visualized to show the effect of the change of the blade twist and chord on the change of the aerodynamic behaviors. The method is validated for a canonical straight blade with uniform lift coefficient and axial induction distributions. The results show an excellent agreement with those obtained by PROPID, which is a blade element momentum theory-based inverse design code. The National Renewable Energy Laboratory Phase VI blade is used to validate the method for a straight blade with non-uniform distributions of the lift coefficient and axial induction. The method is also applied successfully to a non-straight blade design with a non-planar wing tip. A noticeable change in the twist and chord for this non-straight blade is seen compared with a straight blade. Finally, the inverse design code is used to make a large rotor blade, and the power output generated by this blade is computed. 
## 
## Topic 9: Energy Systems
## V18: 10.1002/we.2794
## Aeroelastic parked testing of a unique downwind two-bladed subscale rotor was completed to characterize the response of an extreme-scale 13-MW turbine in high-wind parked conditions. A 20% geometric scaling was used resulting in scaled 20-m-long blades, whose structural and stiffness properties were designed using aeroelastic scaling to replicate the nondimensional structural aeroelastic deflections and dynamics that would occur for a lightweight, downwind 13-MW rotor. The subscale rotor was mounted and field tested on the two-bladed Controls Advanced Research Turbine (CART2) at the National Renewable Energy Laboratory&#39;s Flatiron Campus (NREL FC). The parked testing of these highly flexible blades included both pitch-to-run and pitch-to-feather configurations with the blades in the horizontal braked orientation. The collected experimental data includes the unsteady flapwise root bending moments and tip deflections as a function of inflow wind conditions. The bending moments are based on strain gauges located in the root section, whereas the tip deflections are captured by a video camera on the hub of the turbine pointed toward the tip of the blade. The experimental results are compared against computational predictions generated by FAST, a wind turbine simulation software, for the subscale and full-scale models with consistent unsteady wind fields. FAST reasonably predicted the bending moments and deflections of the experimental data in terms of both the mean and standard deviations. These results demonstrate the efficacy of the first such aeroelastically scaled turbine test and demonstrate that a highly flexible lightweight downwind coned rotor can be designed to withstand extreme loads in parked conditions. 
## 
## Topic 9: Energy Systems
## V19: 10.1016/j.energy.2023.128832
## Unknown and changeable driving conditions of off-road hybrid electric vehicle (HEV) challenge its energy management strategy (EMS). To tackle this issue, the paper develops a real-time adaptive strategy for off-road HEVs through decision-time planning (DTP), which is a unique method of model-based reinforcement learning (MBRL). First, the MBRL framework for the energy management problem is established, including a RL-oriented model and the DTP algorithm. The RL model consists of a deterministic nonlinear state space model and a stochastic recursive Markov Chain (MC), and the latter is constructed online and updated constantly according to new observations, which can reflect the driving condition precisely. Then, the DTP algorithm is detailed and applied. Instead of learning an overall policy for an entire driving cycle, it seeks to learn the optimal action for each encountered vehicle state, which improves the learning efficiency and realizes the real-time adaptive EMS. In the simulation, assuming that no prior information of the driving conditions is known, the proposed EMS only takes about 1–3% more fuel and 10% more battery life than dynamic programming in both off-road driving conditions and standard road cycles. The EMS significantly outperforms traditional Q-learning and rule-based strategy, verifying its optimization capability and adaptability. 
## 
## Topic 9: Energy Systems
## V20: 10.1016/j.egyr.2022.11.119
## Aiming at the uncertain optimization problem of AC/DC hybrid distribution network under the coordination of source, grid load and storage, an AC/DC hybrid distribution network operation scheme based on two stage affinely adjustable robust optimization was proposed in this paper. Firstly, the controllable model of distribution generation (DG), the battery energy storage model with cycle life degradation and the ZIP load model were considered, and the optimization model of AC/DC hybrid distribution network economic operation was constructed. Then the ellipsoid set was used to describe the uncertainty of DG output, and the linear decision-making mechanism of decision variables and uncertainty variables of DG output in the optimization model was formed by combining linear affine strategy, Finally, the robust optimization model of AC/DC hybrid distribution network was transformed into a deterministic mixed integer second-order cone programming model by dual transformation. The effectiveness and advantages of the affinely adjustable robust optimization method proposed in this paper were compared and verified in the two modified IEEE bus system. 
## 
## Topic 9: Energy Systems
## V21: 10.1016/j.apenergy.2021.116929
## Efficient and accurate photovoltaic (PV) modeling plays an important role in optimal evaluation and operation of PV power systems. Using current–voltage (I-V) curves measured at different operating conditions, a novel extreme learning machine (ELM) based modeling method is proposed for characterizing the electrical behavior of PV modules, which features high training speed and generalization performance. Firstly, a voltage-current grid based method is used to resample each raw measured I-V curve for reducing data redundancy of I-V curves, a slope change based detection method is proposed to exclude the abnormal I-V curves for improving the data quality, and an irradiance-temperature grid based method is applied to downsample the dataset. Secondly, a single hidden-layer feedforward neural network (SLFN) is proposed as the model structure, which is then trained by the ELM learning algorithm. Particularly, the configuration of the ELM is optimized by cross-validation. Finally, the proposed ELM based PV modeling method is verified and tested on the preprocessed large datasets of I-V curves of six PV modules from the National Renewable Energy Laboratory. Moreover, in order to verify the advantage, the ELM based PV modeling is further compared with some commonly used machine learning based methods. Experimental results demonstrate that the proposed ELM based PV modeling method features fast training, high accuracy and generalization performance. The comparison results further indicate that the ELM based model is greatly superior in terms of the training time, and is slightly better than other algorithms in terms of the model accuracy and generalization performance. 
## 
## Topic 9: Energy Systems
## V22: 10.1109/TPWRS.2020.2993920
## Energy storage systems (ESSs) have been applied in power systems for a long time. It participates in the grid frequency control at two levels, the primary frequency control and the secondary frequency control. In this paper, a control strategy that integrates one synchronous generator (SG) and ESS as one single generation unit named flexible generator (FG) is proposed, allowing ESS to participate in primary frequency control. The main trick is that the proposed control strategy not only adopts SG&#39;s frequency as the ESS control input but also feeds back FG&#39;s total output power to control the ESS. FGs have more substantial damping and stability than SGs. The proposed control strategy has a superior performance compared to traditional PID control and fuzzy control strategies of ESSs. The flexible control parameters are determined by the linear quadratic regulator (LQR) with disturbance suppression. The ESS&#39;s capacity is discussed and determined. Simulation results of the New England 39-bus system verify the advantages of FGs both in load changes and faults. The HIL experiment results also illustrate the applicability and effectiveness of the flexible control. 
## 
## Topic 9: Energy Systems
## V23: 10.1016/j.renene.2020.05.060
## In this paper, passive reinforcement learning (RL) solved by particle swarm optimization policy (PSO–P) is used to handle an adaptive neuro-fuzzy inference system (ANFIS) type-2 structure with unsupervised clustering for controlling the pitch angle of a real wind turbine (WT). The proposed control scheme is based on gain-scheduled reinforcement learning recurrent ANFIS type 2 (GS-RL-RANFIST2) pitch angle controller to maintain the rotor speed at its rated value while smoothing the output power and the performance of the pitch angle system. The practical application of the proposed controller is evaluated by using FAST tool for a real 600 kW WT equipped with a synchronous generator with a full-size power converter (CART3, located at the National Renewable Energy Laboratory, NREL), whose results are compared with those obtained by a gain corrected proportional integral (GC-PI) controller. The results demonstrate that the GS-RL-RANFIST2, which sets the nonlinear characteristics of the system automatically and waves more uncertainties in the windy conditions, allows to increase the energy capture and smooth the output power fluctuation, and therefore, to improve the control and response of the WT. 
## 
## Topic 9: Energy Systems
## V24: 10.1109/TEC.2023.3258919
## This paper proposes a decentralized and coordinated voltage and frequency (V-f) control framework for islanded microgrids, with full consideration of the limited capacity of distributed energy resources (DERs) and V-f dependent load. First, the concept of DER inadequacy is illustrated with the challenges it poses. Then, a decentralized and coordinated control framework is proposed to regulate the output of inverter-based generations and reallocate limited DER capacity for V-f control. The control framework is composed of a power regulator and a V-f regulator, which generates the supplementary signals for the primary controller. The power regulator regulates the output of grid-forming inverters according to the real-time capacity constraints of DERs, while the V-f regulator improves the V-f deviation by leveraging the load sensitivity to V-f. Next, the static feasibility and small signal stability of the proposed method are rigorously proven through mathematical formulation and eigenvalue analysis. Finally, a MATLAB-Simulink simulation demonstrates the functionalities of the control framework. A few goals are fulfilled within the decentralized and coordinated framework, such as making the best use of limited DERs&#39; capacity, enhancing the DC side stability of inverter-based generations, and reducing involuntary load shedding. 
## 
## Topic 9: Energy Systems
## V25: 10.17775/CSEEJPES.2020.01230
## This paper focuses on optimal voltage regulator (VR) planning to maximize the photovoltaic (PV) energy integration in distribution grids. To describe the amount of dynamic PV energy that can be integrated into the power system, the concept of PV accommodation capability (PVAC) is introduced and modeled with optimization. Our proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization problem. In the upper-level problem, VR planning decisions and PVAC are determined via mixed integer linear programming (MILP) before considering uncertainty. Then in the lower-level problem, the feasibility of first-level results is checked by critical network constraints (e.g. voltage magnitude constraints and line capacity constraints) under uncertainties considered by time-varying loads and PV generations. In this paper, these uncertainties are represented in the form of operational scenarios, which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction algorithm, The modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed model. The results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning. 
## 
## Topic 10: Renewable Energy
## V1: 10.1016/j.energy.2016.10.030
## Small modular reactors (SMRs) may be an option to cover the electricity needs of isolated regions, distributed generation grids and countries with small electrical grids. Previous analyses show that the overnight capital cost for SMRs is between 4500 US$/kW and 5350 US$/kW, which is between a 6% and a 26% higher than the average cost of a current large nuclear reactor. This study analyzes the economic competitiveness of small modular reactors against thermal plants using coal and natural gas combined cycle plants. To assess the economic competitiveness of SMRs, three overnight capital costs are considered 4500 US$/kW, 5000 US$/kW and 5350 US$/kW along with three discount rates for each overnight cost considered, these are 3, 7, and 10%. To compare with natural gas combined cycle (CC) units, four different gas prices are considered, these are 4.74 US$/GJ (5 US$/mmBTU), 9.48 US$/GJ (10 US$/mmBTU), 14.22 US$/GJ (15 US$/mmBTU), and 18.96 US$/GJ (20 US$/mmBTU). To compare against coal, two different coal prices are considered 80 and 120 US$/ton of coal. The carbon tax considered, for both CC and coal, is 30 US$/ton CO2. The results show what scenarios make SMRs competitive against coal and/or combined cycle plants. In addition, because the price of electricity is a key component to guarantee the feasibility of a new project, this analysis calculates the price of electricity for the economically viable deployment of SMRs in all the above scenarios. In particular, this study shows that a minimum price of electricity of 175 US$/MWh is needed to guarantee the feasibility of a new SMR, if its overnight capital cost is 5350 US$/kWe and the discount rate is 10%. Another result is that when the price of electricity is around 100 US$/MWh then the discount rate must be around 7% or less to provide appropriate financial conditions to make SMRs economically feasible. 
## 
## Topic 10: Renewable Energy
## V2: 10.1016/j.apenergy.2019.05.001
## Clean energy deployment scenarios typically assume a diverse mix of low-emission generation. However, because of historically low natural gas prices and cost reductions for onshore wind and photovoltaic technologies, recent new capacity deployment, especially in the United States, has come largely from these three generation sources. Here, we quantify competitive cost targets for three less-common zero-emission generation technologies—nuclear, concentrating solar, and offshore wind—under a range of future conditions. We determine the cost targets based on the technologies’ system value, such that technology value equals or exceeds its cost. Using a capacity expansion model that develops system-wide, economically optimal generation scenarios of the United States power sector, we find that nuclear, concentrating solar, and offshore wind would need to attain levelized cost ranges of $53–84/MWh, $65–91/MWh, and $39–77/MWh, respectively, to achieve 1% additional grid penetration. We refer to this metric as the “required cost,” which is a technology&#39;s levelized cost of energy needed to achieve a specific penetration level. Higher penetrations lead to lower required costs as lower-cost generation is displaced. We estimate that to reach 10% penetration, the required cost for nuclear declines to $45–76/MWh. Because of resource variability and increased transmission needs for concentrating solar power and offshore wind, even lower costs are needed to reach higher penetrations: required cost ranges to achieve 10% penetration are estimated to be $30–56/MWh and $18–40/MWh for these two technologies respectively. Our analysis informs research and development priorities and the design of policies supporting clean technologies. 
## 
## Topic 10: Renewable Energy
## V3: 10.1016/j.eneco.2018.03.021
## The U.S. economy decarbonization over the next 35 years requires a large transformation of the energy system. The main finding of this study is that it is technically feasible to achieve 80% greenhouse gas (GHG) emissions reduction below the 2005 levels by 2050 through deployment of existing or near-commercially available technologies. GHG reductions are primarily achieved through high levels of electricity sector decarbonization, electrification of end uses, and exchange of the remaining end-uses to lower carbon fuels such as natural gas. However, deep decarbonization by 2050 triggers very high marginal CO2 reduction costs, unless significant cost reductions of zero and near-zero carbon technologies occur. The results show that CO2 reduction policies accelerate the deployment of renewables and carbon capture and storage (CCS) only in the scenarios where the decarbonization policies are more stringent than those in the Clean Power Plan (CPP). Electricity generation mixes in the reference scenarios are largely dependent on the price of natural gas, but also show significant sensitivity to cost reductions in CCS. When increased CCS learning rates are incorporated into the model runs, more CCS is optimal in the medium-term and long-term future in the scenarios with CO2 constraints. An 80% reduction scenario also prompts electrification in end-use demand sectors, creating even more dependence on CO2 management in the electricity generation sector. 
## 
## Topic 10: Renewable Energy
## V4: 10.1016/j.jclepro.2018.08.321
## We compare the cost of maintaining a proposed subsidy for New York&#39;s three upstate nuclear power plants with the cost of replacing the plants with renewable technologies from 2016 to 2050. Keeping nuclear operating with subsidy until 2050 is the most expensive option, costing $32.4 billion (2014 USD) over that period in the base business as usual case. The least expensive option is to shut down nuclear today and replace it with onshore wind, saving $7.9 billion. All analyzed renewable scenarios lead to 20.1 to 27.4 Mt CO2 greater life-cycle emission reductions. In addition, re-investing the cost savings of the renewable scenarios into additional onshore wind increase CO2 savings up to 32.5 Mt. 
## 
## Topic 10: Renewable Energy
## V5: 10.1016/j.enpol.2011.02.042
## The electricity sector is the largest source of greenhouse gas emissions (GHGs) in the U.S. Many states have passed and Congress has considered Renewable Portfolio Standards (RPS), mandates that specific percentages of electricity be generated from renewable resources. We perform a technical and economic assessment and estimate the economic costs and net GHG reductions from a national 25 percent RPS by 2025 relative to coal-based electricity. This policy would reduce GHG emissions by about 670 million metric tons per year, 11 percent of 2008 U.S. emissions. The first 100 million metric tons could be abated for less than $36/metric ton. However, marginal costs climb to $50 for 300 million metric tons and to as much as $70/metric ton to fulfill the RPS. The total economic costs of such a policy are about $35 billion annually. We also examine the cost sensitivity to favorable and unfavorable technology development assumptions. We find that a 25 percent RPS would likely be an economically efficient method for utilities to substantially reduce GHG emissions only under the favorable scenario. These estimates can be compared with other approaches, including increased R&amp;D funding for renewables or deployment of efficiency and/or other low-carbon generation technologies. © 2011 Elsevier Ltd. 
## 
## Topic 10: Renewable Energy
## V6: 10.1016/j.renene.2021.05.118
## This study assesses Indonesia power system&#39;s transition pathway to reach 100% renewable energy in 2050. The pathway is determined based on least-cost optimisation in the TIMES model comparing 27 power plants and 3 energy storage technologies and using hourly demand and supply operational profile using 24-h time slices. From this study, it can be concluded that nuclear and solar PV utility-scale will play an essential role up to 16% and 70% of total electricity production, corresponding to 1396 TWh in 2050. The investment cost in 2050 is three times higher, and the emission is one-sixth lower than in Business as Usual, equal to 95 billion USD and 215 million tons of CO2-eq. The RE mix based on current policy generates a higher CO2 abatement cost, 120 USD/ton CO2-eq in 2050. The optimistic demand projection will increase the coal by 82% in Business as Usual also nuclear and solar PV utility-scale of about 126% and 62% in 100% RE, respectively. The exclusion nuclear in power system increase the installed capacity of solar PV utility-scale and battery, increase land requirement by 78%–83%, increase the variability of supply from other power plants and batteries, and increase 9.7% of electricity production cost. 
## 
## Topic 10: Renewable Energy
## V7: 10.1016/j.enpol.2010.11.020
## We have developed a state-scale version of the MARKAL energy optimization model, commonly used to model energy policy at the US national scale and internationally. We apply the model to address state-scale impacts of a renewable electricity standard (RES) and a carbon tax in one southeastern state, Georgia. Biomass is the lowest cost option for large-scale renewable generation in Georgia; we find that electricity can be generated from biomass co-firing at existing coal plants for a marginal cost above baseline of 0.2-2.2. cents/kWh and from dedicated biomass facilities for 3.0-5.5. cents/kWh above baseline. We evaluate the cost and amount of renewable electricity that would be produced in-state and the amount of out-of-state renewable electricity credits (RECs) that would be purchased as a function of the REC price. We find that in Georgia, a constant carbon tax to 2030 primarily promotes a shift from coal to natural gas and does not result in substantial renewable electricity generation. We also find that the option to offset a RES with renewable electricity credits would push renewable investment out-of-state. The tradeoff for keeping renewable investment in-state by not offering RECs is an approximately 1% additional increase in the levelized cost of electricity. © 2010 Elsevier Ltd. 
## 
## Topic 10: Renewable Energy
## V8: 10.1016/j.renene.2019.06.079
## Transition of Iran&#39;s power system from 2015 to 2050 through three scenarios was modelled. Two scenarios present a transition pathway towards a fully renewable run power system with different involved sectors (power only, power sector coupled with desalination and non-energetic gas sectors). The third scenario is based on the country&#39;s current policies. The energy model performs an hourly resolution to guarantee meeting energy demand for every hour of the whole year. It is found that renewable energy resources in Iran can satisfy 625 TWh of power sector demand in 2050. Further, it is technically and economically feasible that electricity demand for supplying 101 million m3 desalinated water and 249 TWhLHV synthetic natural gas for non-energetic industrial gas demand can be supplied via renewable resources. A 100% renewable power system with 54 €/MWhel levelised cost of electricity (LCOE) is more cost-effective than the current power system in Iran with 88.3 €/MWhel LCOE in 2015. LCOE of the system can decrease further and reach to 41.3 €/MWhel in 2050 via sector coupling. On the other hand, the current policies of the country lead to an inefficient power system with a LCOE of 128 €/MWhel and 188 Mt/a emitted CO2 in 2050. 
## 
## Topic 10: Renewable Energy
## V9: 10.1016/j.esr.2022.101042
## At COP26, India has committed to achieve net zero emissions by 2070. For economy wide net zero, the power system should be first to attain it. This paper explores the role of different technologies, CO2 capture and storage (CCS), nuclear, solar PV and thermal, battery storage, pumped storage, hydro etc. along with energy efficiency in doing so by different target years, 2050 and 2060 and their economic implications. With more intermittent renewables, the issue of balancing hourly demand-supply of both energy and power becomes critical for ensuring the feasibility of a pathway. Hourly availability of different renewable technologies is considered along with hourly variations in electricity demand. Three scenarios are analysed, Business-as-Usual (BAU) scenario assumes current policies to continue, and two Net Zero (NZ) scenarios to achieve net zero emissions by 2050 and 2060. Solar PV, wind onshore and offshore, battery storage are the dominant technologies in achieving net zero emissions. Solar and wind together contribute 85% and 90% of the total generation capacity in 2050 and 2060, respectively. Dispatchable technologies like Coal plant with CCS and nuclear are also important. Results specify Battery with storage hour specifications (1 h, 2 h etc.), so one can plan storage in details. Decarbonisation has significant additional cost and investment requirement over the Business-As-Usual scenario. Respective additional cumulative investment requirements (2030–60) are about 1.6 trillion USD and 1.4 trillion USD in two NZ scenarios. The required policies and measures are also discussed. 
## 
## Topic 10: Renewable Energy
## V10: 10.1016/j.enpol.2016.10.026
## This study investigates the potential cost and emissions reductions that result from an increase in electricity transmission capacity between Canada&#39;s two westernmost provinces: Alberta, a fossil fuel dominated jurisdiction, and British Columbia, a predominantly hydroelectric jurisdiction. A bottom-up model is used to find the least cost electricity generation mix in Alberta and British Columbia under different carbon policies. The long-term evolution of the electricity system is determined by minimizing net present cost of electricity generation for the time span of 2010–2060. Different levels of intertie capacity expansion are considered together with a variety of carbon tax and carbon cap scenarios. Results indicate that increased intertie capacity reduces the cost of electricity and emissions under carbon pricing policies. However, the expandable intertie does not encourage greater adoption of variable renewable generation. Instead, it is used to move low-cost energy from the United States to Alberta. The optimal intertie capacity and cost reduction of increased interconnectivity increases with more restrictive carbon policies. 
## 
## Topic 10: Renewable Energy
## V11: 10.1016/j.enpol.2020.112013
## Over the past several years state energy policies have been evolving rapidly, and are more frequently including higher targets, including 100% clean energy targets. This study assesses the aggregate impacts of state clean energy standards and emissions policies on national electricity generation, power sector carbon dioxide (CO2) emissions, and electricity prices and system costs. To do so, we apply the Regional Energy Deployment System (ReEDS) model, which is a detailed electric sector capacity expansion model, to evaluate scenarios with and without state policies and using a range of renewable energy technology cost projections. Across the scenarios analyzed, we find that the state policies drive 1.9%–10.7% of total nationwide clean energy and reduce cumulative power sector CO2 emissions by 2.6%–5.4% over the 2020–2050 study period. This incremental generation is predominantly, but not exclusively, from renewable energy technologies. In most cases, the state policies result in increases to electricity prices and electricity system costs, and policy costs are sensitive to the future cost of clean energy technologies. Across all scenarios, the levelized cost of incremental policy-driven clean energy generation is estimated to be $17–38/MWh and the average cost of CO2 abatement from the state-level policies is estimated to be $29–74/metric ton. 
## 
## Topic 10: Renewable Energy
## V12: 10.1016/j.energy.2015.02.066
## Based on a data-intensive weather-driven modelling approach, technically and economically optimal designs are derived for a simplified, highly renewable pan-European electricity system, which minimise the need for backup energy, backup capacity, transmission capacity and the levelised system cost of delivered electricity. The overall cost-optimal design, based on standard cost assumptions, relies on synchronised backup across the transmission grid and comes with a renewable penetration of 50% with a rather high wind fraction of 94%. Given the current European electricity consumption, this corresponds to 600GW rated wind power capacities, 60GW installed solar power capacities, 320GW conventional backup power capacity, and about five times today&#39;s installed transmission capacities. A sensitivity analysis reveals that the design and cost of the optimal system depend mostly on the assumed cost of wind capacity and fuel for backup energy. Lower costs for wind capacity, higher costs for backup energy and usage of otherwise curtailed excess electricity generation lead to a strong increase of the optimal renewable penetration. The sensitivity analysis is also used to find that a CO2 tax of over 100€/ton would be needed for the economic viability of carbon capture and sequestration. 
## 
## Topic 10: Renewable Energy
## V13: 10.1016/j.ijhydene.2019.03.173
## A power grid with a lower global warming impact has the potential to extend its benefits to energy systems that conventionally do not utilize electricity as their primary energy source. This study presents the case of Ontario where the role of complementing policies in transitioning electricity systems is assessed. The policy cost to incentivize surplus low emission electricity via an established mechanism for the transportation sector has been estimated (Electric and Hydrogen Vehicle Incentive Program). It is estimated that the 9056 (4760 battery and 4296 plug-in hybrid) electric vehicles that qualified for incentives from the provincial government at the end of 2016 vehicles cost $732.5-$883.9 to reduce a tonne of CO2,e emissions over an eight year lifetime. This is then compared with the potential cost incurred by two power to gas energy hubs that utilize clean surplus electricity from the province to offset emissions within the natural gas sector. The use of hydrogen-enriched natural gas and synthetic natural gas (SNG) offsets emissions at $87.8 and $228.7 per tonne of CO2,e in the natural gas sector. This analysis highlights the potential future costs for incentivizing new clean technologies such as electric vehicles and power to gas energy hubs in jurisdictions with a transitioning electricity system. 
## 
## Topic 10: Renewable Energy
## V14: 10.1016/j.ijggc.2021.103450
## Carbon capture and storage (CCS) is a prominent mitigation technology in many of the pathways to achieve global net-zero carbon-dioxide (CO2) emissions. Despite this proposed importance, only one operational power facility in the U.S. is currently equipped with CCS. Further CCS capacity may be promoted with recently-enhanced tax credits for carbon sequestration in Section 45Q of the U.S. Internal Revenue Code. In this paper, we expand the unit-specific techno-economic model ESTEAM to include coal-fired and natural gas combined cycle (NGCC) retrofitted CCS capacity and new NGCC CCS capacity to evaluate CCS for sequestration in the projected 2030 U.S. fossil-fuel fleet. Using this model in a parametric study, we conclude that unique credit levels for each CCS option are required for each option to separately achieve the same percent generation level of the projected 2030 net generation. We further find that increasing the credit duration can lower the CO2 avoided cost for the fleets and increase CCS-capacity bridging from 2030 to 2050. Overall, we determine that a lower avoided cost for immediate sequestration is achieved through promoting new and existing NGCC CCS. 
## 
## Topic 10: Renewable Energy
## V15: 10.1016/j.ijhydene.2022.12.058
## Hydrogen has a potential role in helping the world for obtaining net-zero emission/emission-free energy systems by 2050 and restrict global warming by 1.5 °C because it can subside 80 gigatons (GT) of CO2 by 2050. This causes the utilization of 660 million metric tons (MT) of low-carbon hydrogen and renewables up to 2050, almost equal to 22% of global energy demand. In the coming decades, the need for very high clean hydrogen production to fulfill the decarbonization role for net zero emission. About 4.5 to 6.5 terawatts (TW) of renewable power, about 3–4 TW of electrolysis, 40–280 MT of minimal carbon hydrogen production reforming capacity, and 1–1.25 GT of CO2 storage infrastructure in a year are required for supplying 660 MT of end uses. In such circumstances, 15–25% of total renewable energy is required for hydrogen till 2050 – a 10X enhancement over the present installed capacity (2.8 TW). 
## 
## Topic 10: Renewable Energy
## V16: 10.1016/j.apenergy.2022.119679
## Decarbonizing the electricity system to zero-carbon emission is crucial for climate change mitigation. Previous studies have shown that such a transition in the United States (U.S.) may lead to higher system cost compared to a business-as-usual case, but it is not well-known how the cost of electricity generation varies at sub-regional level under the transition, and studies have rarely evaluated the trade-off between the cost and avoided climate damages, as well as the potential roles of negative emission technologies (NETs) in the electricity decarbonization. Here, we present a regionally resolved national model to quantify the cost of decarbonizing the U.S. electricity system under a set of possible scenarios. The results show that, compared to the reference scenario without a decarbonization policy, reaching zero CO2 emission by 2050 would incur, depending on the scenarios, 335–494 billion USD additional cost to the U.S. electric power sector during 2020–2050. The regional costs of electricity generation ranges from 2.4 to 4.7 cent/kWh, largely due to the generation profiles and renewable resources availability of those regions. The additional costs can be translated to an average CO2 abatement cost of 29–59 USD/metric ton CO2 (with 2%–7% discount rates), which are comparable to the social cost of carbon in the literature at around 4% discount rate. The results also show that the cost of mitigating the last few percent CO2 emission from the U.S. electricity system may exceed the costs of NETs, indicating an opportunity for NETs to contribute to electricity decarbonization. 
## 
## Topic 10: Renewable Energy
## V17: 10.1016/j.apenergy.2015.05.071
## Future generation portfolios including varying quantities of gas-fired and renewable generation were compared on the basis of expected costs, cost risk and greenhouse gas emissions, with a view to understanding the merits and disadvantages of gas and renewable technologies. A Monte-Carlo based generation portfolio modelling tool was applied to take into account the effects of highly uncertain future gas prices, carbon pricing policy and electricity demand. Results suggest that portfolios sourcing significant quantities of energy from gas-fired generation in 2030 and 2050 are likely to be significantly higher cost and significantly higher risk than the other alternatives considered. High gas portfolios also do not achieve the greenhouse gas (GHG) emissions reductions levels that appear required to avoid dangerous global warming. For example, portfolios that source 95% of energy from gas-fired generation in 2050 experience expected generation costs that are $65/MW. h (40%) higher than portfolios that source only 20% of energy from gas-fired generation. These high gas portfolios also exhibit a cost risk (standard deviation in cost) that is three times higher. The lowest cost portfolios in 2050 source less than 20% of energy from gas with the remaining energy sourced from renewables. Even in the absence of a carbon price, the lowest cost portfolio in 2050 sources only 30% of energy from gas-fired generation, with the remaining 70% of energy being sourced from renewable technologies. Results suggest the optimal strategy for minimising costs, minimising cost risk and reducing GHG emission levels in future electricity industries may involve minimising energy sourced from gas, and increasing renewable generation. In the Australian case study considered, the modelling suggests it is appropriate to target renewable energy penetrations approaching 60% of energy by 2030 and 80-100% by 2050. In the lowest cost and lowest risk portfolios, firm capacity is provided primarily by the transition of existing coal-fired plant into a peaking role, and later by further investment in peaking open cycle gas turbine plant. These results are found to be robust to a wide range of assumptions around future carbon prices. 
## 
## Topic 10: Renewable Energy
## V18: 10.1016/j.energy.2017.05.011
## Nuclear power plants generate over 60% of the carbon-free electricity in the U.S. Due to a decrease in electricity prices as a result of the availability of cheaper natural gas and increased low-cost renewables, many of these plants are at risk of premature retirement. If nuclear power plants retire, CO2 emissions in many U.S. states could increase, even while the states comply with EPA legislation aimed at mitigating emissions of greenhouse gases that contribute to climate change. In this paper, we perform a Monte Carlo-based analysis to determine the breakeven price of electricity these plants must secure in order to avoid financial loses between 2015 and 2040, and find median breakeven electricity prices to range between $35 and $73 per MWh. Based on our estimates of future electricity prices under a low natural gas price scenario from the Energy Information Administration, our analysis suggests that U.S. nuclear plants would require between $8 and $44 per MWh (median results) on top of electric sales revenue in order to breakeven. Assuming natural gas plants would replace retired nuclear power plants, we estimate an equivalent cost of avoided CO2 emissions to be $18-$30 per metric ton of avoided CO2 (median results) for multi-reactor nuclear plants, and $47-$97 per metric ton of avoided CO2 (median results) for single-reactor plants. Preserving the existing nuclear power plant fleet, especially multi-reactor plants, is thus a cost effective carbon-avoidance strategy compared to the social cost of carbon. 
## 
## Topic 10: Renewable Energy
## V19: 10.1016/j.renene.2012.07.032
## Electricity consumption of Turkey at the year 2023 is estimated to be around 530,000 GWh. Turkey plans to supply 30% or 160,000 GWh of this demand from renewable energy sources according to the recently avowed government agenda Vision 2023. However, the current installed renewable energy capacity is around 60,000 GWh. Detailed literature analysis showed that only wind and solar energy potential in Turkey can solely supply this demand. In this study, two different scenarios were generated to analyse the cost and environmental impacts of supplying this demand. Scenario 1, which is derived from the official Vision 2023 targets, suggests supplying this demand from wind, solar, geothermal energy and hydropower. The total projected cost based on Scenario 1 is estimated to be $31.000 billion and annual greenhouse gas emissions of 1.05 million tonnes of CO 2 equivalent. According to Scenario 2 or the contrary setup it is assumed that the required demand gap could not be supplied from new renewable energy investments but equally from coal and natural gas. The projected cost is estimated to be around $8.000 billion and annual greenhouse gas emissions at appalling 71.30 million tonnes of CO 2 equivalent. Assuming carbon tax at the year 2023 to be $50 per tonne of CO 2 emitted, supplying the demand from renewable energy sources according to Scenario 1 would generate savings worth nearly $2.175 billion from environmental taxes annually. Thus, making the payback time of the renewable energy investments less than 15 years. © 2012 Elsevier Ltd. 
## 
## Topic 10: Renewable Energy
## V20: 10.1016/j.renene.2013.12.010
## Policy makers face difficult choices in planning to decarbonise their electricity industries in the face of significant technology and economic uncertainties. To this end we compare the projected costs in 2030 of one medium-carbon and two low-carbon fossil fuel scenarios for the Australian National Electricity Market (NEM) against the costs of a previously published scenario for 100% renewable electricity in 2030. The three new fossil fuel scenarios, based on the least cost mix of baseload and peak load power stations in 2010, are: (i) a medium-carbon scenario utilising only gas-fired combined cycle gas turbines (CCGTs) and open cycle gas turbines (OCGTs); (ii) coal with carbon capture and storage (CCS) plus peak load OCGT; and (iii) gas-fired CCGT with CCS plus peak load OCGT. We perform sensitivity analyses of the results to future carbon prices, gas prices, and CO2 transportation and storage costs which appear likely to be high in most of Australia. We find that only under a few, and seemingly unlikely, combinations of costs can any of the fossil fuel scenarios compete economically with 100% renewable electricity in a carbon constrained world. Our findings suggest that policies pursuing very high penetrations of renewable electricity based on commercially available technology offer a cost effective and low risk way to dramatically cut emissions in the electricity sector. © 2013 Elsevier Ltd. 
## 
## Topic 10: Renewable Energy
## V21: 10.1016/j.jpowsour.2009.10.024
## California has taken steps to reduce greenhouse gas emissions from the transportation sector. One example is the recent adoption of the Low Carbon Fuel Standard, which aims to reduce the carbon intensity of transportation fuels. To effectively implement this and similar policies, it is necessary to understand well-to-wheels emissions associated with distinct vehicle and fuel platforms, including those using electricity. This analysis uses an hourly electricity dispatch model to simulate and investigate operation of the current California grid and its response to added vehicle and fuel-related electricity demands in the near term. The model identifies the &quot;marginal electricity mix&quot; - the mix of power plants that is used to supply the incremental electricity demand from vehicles and fuels - and calculates greenhouse gas emissions from those plants. It also quantifies the contribution from electricity to well-to-wheels greenhouse gas emissions from battery-electric, plug-in hybrid, and fuel cell vehicles and explores sensitivities of electricity supply and emissions to hydro-power availability, timing of electricity demand (including vehicle recharging), and demand location within the state. The results suggest that the near-term marginal electricity mix for vehicles and fuels in California will come from natural gas-fired power plants, including a significant fraction (likely as much as 40%) from relatively inefficient steam- and combustion-turbine plants. The marginal electricity emissions rate will be higher than the average rate from all generation - likely to exceed 600 gCO2 equiv. kWh-1 during most hours of the day and months of the year - and will likely be more than 60% higher than the value estimated in the Low Carbon Fuel Standard. But despite the relatively high fuel carbon intensity of marginal electricity in California, alternative vehicle and fuel platforms still reduce emissions compared to conventional gasoline vehicles and hybrids, through improved vehicle efficiency. © 2009 Elsevier B.V. All rights reserved. 
## 
## Topic 10: Renewable Energy
## V22: 10.1002/we.2314
## High shares of renewable energy, particularly wind power, were modelled in several future scenarios for the Scottish energy system. In the first part of this work, it was determined that Scotland could produce the equivalent baseload power for supply to England at a lower overall cost (99 €/MWh) than the proposed subsidized price (112 €/MWh) to be paid for electricity generated from the proposed Hinkley Point C nuclear power plant. This cost includes all extra generation capacity and transmission lines. In the second part of this work, it was determined that a 100% renewable energy system could be achieved at an annualized cost of 10.7 b€/a, approximately 8% less than the 11.7 b€/a expected for an energy system composed of 75% renewable energy. In the 100% renewable energy system, cost savings are achieved through effective energy storage, sector integration, and flexible generation from dispatchable renewable energy resources, such as hydropower (1.7 GWe), bioenergy, and synthetic fuels. Complementary resources to 23.4 GWe of wind power also included solar photovoltaics (10.1 GWe), tidal power (1.5 GWe), and wave power (0.3 GWe). It was also determined that carbon capture and utilization would be a preferable strategy to carbon capture and storage for Scotland. Complete defossilization of the Scottish energy system appears feasible by 2050, given the assumptions used in this study. 
## 
## Topic 10: Renewable Energy
## V23: 10.1016/j.energy.2018.01.039
## This paper presents an assessment of alternative, long-term energy supply and low-carbon strategies for the Philippine power sector from 2014 to 2040 using TIMES model. It examines the potential contribution of renewable energy to diversify the Philippine energy supply-mix to meet future electricity demands. The reference scenario compares the impact of four alternative policy goals: (1) carbon tax, (2) targeted renewable-based power generation, (3) limited coal share in supply-mix, and (4) renewables subsidy. The reference scenario shows a significant increase of the share of coal-based power generation and import dependency of fossil-fuel increases from 227 PJ in 2016 to 1073 PJ in 2040. The model results for the alternative policy scenarios show a large potential for renewable energy-based power generation. The alternative policy options show a significant decrease of import dependency in the energy supply-mix for power generation. Most alternative policy scenarios project a higher total system cost, with the exception of the subsidy scenario. System cost increases only 2.6% in the renewables target scenario relative to the reference scenario. However, long-term benefits from investing in the alternative policy options would need to be considered, including diversification of energy supply-mix, improved energy security, and progress toward a low-carbon society. 
## 
## Topic 10: Renewable Energy
## V24: 10.1016/j.energy.2015.08.107
## In this paper, special focus is paid to the long-term adoption of renewable electricity technologies and their implications for emissions reductions in Iran. MESSAGE, as a bottom-up energy supply optimization model, is used to assess the lowest-cost technology options. The potential impacts of transitioning to a renewable electricity supply are quantified, and the investment requirement to achieve a low-carbon generation mix is estimated. Alternative scenarios are defined to evaluate the impact of fossil fuel prices, the carbon tax and government incentives on the utilization of renewable resources, national renewable targets, and emissions reductions. The prioritization of non-hydro renewable energy sources under different circumstances shows that wind is the most promising technology, followed by solar PV (photovoltaic), solar thermal and biogas. The findings demonstrate that the carbon tax would not individually be an effective strategy to reduce emissions. However, the carbon tax coupled with direct renewable subsidies would be more cost-effective, especially while fossil fuel prices are low. In the case of higher fossil fuel prices, the cost-effectiveness of carbon taxes at reducing emissions is not significantly influenced by the level of renewable subsidies. 
## 
## Topic 10: Renewable Energy
## V25: 10.1016/j.energy.2018.09.187
## In this study, the authors developed an Optimal Power Generation Mix model, which takes into account the supply chain of imported and domestically produced hydrogen, also modeling the intermittency of renewable energy at a 10-min resolution, and applied it to the case of Japan, to investigate quantitatively the possibility of achieving zero emission in 2050. Even if the costs of wind and solar PV decline drastically towards 2050 and the huge potentials that have been assumed in the literature are realized, the total system costs escalate significantly with very high shares of intermittent renewables. Since the use of hydrogen produced by excess electricity from renewable power generation sources can only make a slight contribution to reducing this escalation, it would be invaluable to introduce at least a significant amount of electricity generated by “zero-emission thermal power” technologies, including CO2-free imported hydrogen or conventional thermal power generation with carbon capture and sequestration (CCS). Nuclear power is also estimated as being effective in reducing the cost hike associated with achieving zero emissions. The results of this study could contribute to giving insights regarding global deployment of hydrogen-related technologies, as well as to presenting a frame of reference for Japan&#39;s future energy policies.</code></pre>
<pre class="r"><code>ids &lt;- docs$index

ids_df &lt;- ids |&gt;
    as.data.frame() |&gt;
    pivot_longer(everything(), names_to = &quot;topic&quot;, values_to = &quot;index&quot;)

ids_df$scopus_id &lt;- abs_ps$article_scopus_id[ids_df$index]
ids_df$abstract_text &lt;- abs_ps$abstract_text[ids_df$index]
ids_df$article_doi &lt;- abs_ps$article_doi[ids_df$index]

# 2.2 Mean Doc-topic proportions

dty &lt;- doc_topic_df |&gt;
    group_by(article_year) |&gt;
    summarise(
        t1 = mean(Topic1),
        t2 = mean(Topic2),
        t3 = mean(Topic3),
        t4 = mean(Topic4),
        t5 = mean(Topic5),
        t6 = mean(Topic6),
        t7 = mean(Topic7),
        t8 = mean(Topic8),
        t9 = mean(Topic9),
        t10 = mean(Topic10)
    ) |&gt;
    pivot_longer(starts_with(&quot;t&quot;), names_to = &quot;topic&quot;, values_to = &quot;mean&quot;)

dty &lt;- dty |&gt;
    left_join(topic_labs, by = &quot;topic&quot;) |&gt;
    mutate(lab_fac = factor(lab, levels = lab_order))


# 2.3 Mean Doc-topic proportions over time

mean_dtp &lt;- data.frame(
    mean = dtp,
    topic = names(dtp)
) |&gt;
    left_join(topic_labs, by = &quot;topic&quot;) |&gt;
    mutate(lab_fac = factor(lab, levels = lab_order))


dtp_df &lt;- doc_topic_df |&gt;
    select(docnum, article_year, starts_with(&quot;Topic&quot;)) |&gt;
    pivot_longer(starts_with(&quot;Topic&quot;), names_to = &quot;topic&quot;, values_to = &quot;dtp&quot;)

dtp_df &lt;- dtp_df |&gt;
    left_join(topic_labs, by = &quot;topic&quot;) |&gt;
    mutate(lab_fac = factor(lab, levels = lab_order))

dat_avgs_dpt &lt;- dtp_df |&gt;
    group_by(article_year, lab_fac) |&gt;
    summarise(dtp = mean(dtp)) |&gt;
    ungroup()</code></pre>
<pre><code>## `summarise()` has grouped output by &#39;article_year&#39;. You can
## override using the `.groups` argument.</code></pre>
<pre class="r"><code># filter topics mentioned in paper
dat_avgs_dpt |&gt;
    filter(lab_fac %in% c(&quot;Renewable Energy&quot;, &quot;Implementation&quot;)) |&gt;
    filter(article_year %in% c(2010, 2023))</code></pre>
<pre><code>## # A tibble: 4 × 3
##   article_year lab_fac            dtp
##   &lt;chr&gt;        &lt;fct&gt;            &lt;dbl&gt;
## 1 2010         Renewable Energy 0.212
## 2 2010         Implementation   0.200
## 3 2023         Renewable Energy 0.119
## 4 2023         Implementation   0.116</code></pre>
<pre class="r"><code>table(dtp_df$lab_fac)</code></pre>
<pre><code>## 
## Renewable Energy   Implementation   Sustainability   Energy Systems 
##            40468            40468            40468            40468 
##          Economy        Emissions          Biomass          Ecology 
##            40468            40468            40468            40468 
##        Consumers Household Energy 
##            40468            40468</code></pre>
<pre class="r"><code># Figure 4
dtp_year_plot &lt;- dtp_df |&gt;
    ggplot(aes(x = article_year, y = dtp, group = lab_fac)) +
    stat_summary(fun.data = &quot;mean_cl_normal&quot;, geom = &quot;ribbon&quot;, fill = &quot;grey80&quot;) +
    stat_summary(fun = mean, geom = &quot;point&quot;) +
    stat_summary(fun = mean, geom = &quot;line&quot;) +
    geom_hline(data = mean_dtp, aes(yintercept = mean), linetype = &quot;dashed&quot;) +
    coord_cartesian(ylim = c(0, 0.25)) + # &quot;zoom&quot; into plot
    scale_x_discrete(breaks = c(2011, 2013, 2015, 2017, 2019, 2021, 2023)) +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    facet_wrap(~lab_fac,
        nrow = 2, scales = &quot;free_x&quot;,
        labeller = label_wrap_gen(width = 20)
    ) +
    labs(x = &quot;&quot;, y = &quot;Mean Document-Topic Prevalence per Year\n(and 95% Confidence Intervals)&quot;) +
    theme(
        axis.text.x = element_text(angle = 90),
        strip.text.x = element_text(size = 13)
    )
dtp_year_plot</code></pre>
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" width="672" /></p>
<pre class="r"><code>ggsave(dtp_year_plot, filename = &quot;fig_04.png&quot;, width = 9.5, height = 7, dpi = 300)
ggsave(dtp_year_plot, filename = &quot;fig_04.pdf&quot;, width = 9.5, height = 7)
ggsave(dtp_year_plot, filename = &quot;fig_04.eps&quot;, width = 9.5, height = 7)


## Print sessionInfo() in Markdown html report
sessionInfo()</code></pre>
<pre><code>## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.3
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Dublin
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] Hmisc_5.2-2               patchwork_1.3.0          
##  [3] furrr_0.3.1               future_1.34.0            
##  [5] stm_1.3.7                 quanteda.textstats_0.97.2
##  [7] newsmap_0.9.0             xtable_1.8-4             
##  [9] tidycomm_0.4.1            mltest_1.0.1             
## [11] here_1.0.1                rio_1.2.3                
## [13] lubridate_1.9.4           forcats_1.0.0            
## [15] stringr_1.5.1             dplyr_1.1.4.9000         
## [17] purrr_1.0.2               readr_2.1.5              
## [19] tidyr_1.3.1               tibble_3.2.1             
## [21] ggplot2_3.5.1             tidyverse_2.0.0          
## [23] quanteda_4.2.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1    farver_2.1.2        R.utils_2.12.3     
##  [4] fastmap_1.2.0       digest_0.6.37       rpart_4.1.23       
##  [7] timechange_0.3.0    lifecycle_1.0.4     cluster_2.1.6      
## [10] magrittr_2.0.3      compiler_4.4.2      rlang_1.1.5        
## [13] sass_0.4.9          tools_4.4.2         utf8_1.2.4         
## [16] yaml_2.3.10         data.table_1.16.4   knitr_1.49         
## [19] labeling_0.4.3      stopwords_2.3       htmlwidgets_1.6.4  
## [22] bit_4.5.0.1         abind_1.4-8         withr_3.0.2        
## [25] foreign_0.8-87      R.oo_1.27.0         nnet_7.3-19        
## [28] grid_4.4.2          colorspace_2.1-1    lm.beta_1.7-2      
## [31] globals_0.16.3      scales_1.3.0        cli_3.6.3          
## [34] rmarkdown_2.29      ragg_1.3.3          generics_0.1.3     
## [37] rstudioapi_0.17.1   tzdb_0.4.0          cachem_1.1.0       
## [40] splines_4.4.2       parallel_4.4.2      matrixStats_1.5.0  
## [43] base64enc_0.1-3     vctrs_0.6.5         Matrix_1.7-1       
## [46] slam_0.1-55         jsonlite_1.8.9      carData_3.0-5      
## [49] car_3.1-3           ISOcodes_2024.02.12 hms_1.1.3          
## [52] bit64_4.5.2         Formula_1.2-5       htmlTable_2.4.3    
## [55] listenv_0.9.1       systemfonts_1.1.0   jquerylib_0.1.4    
## [58] parallelly_1.42.0   glue_1.8.0          codetools_0.2-20   
## [61] stringi_1.8.4       gtable_0.3.6        munsell_0.5.1      
## [64] pillar_1.10.1       htmltools_0.5.8.1   R6_2.5.1           
## [67] textshaping_0.4.1   rprojroot_2.0.4     evaluate_1.0.1     
## [70] lattice_0.22-6      R.methodsS3_1.8.2   backports_1.5.0    
## [73] bslib_0.8.0         Rcpp_1.0.14         fastmatch_1.1-6    
## [76] nlme_3.1-166        nsyllable_1.0.1     gridExtra_2.3      
## [79] checkmate_2.3.2     mgcv_1.9-1          xfun_0.49          
## [82] pkgconfig_2.0.3</code></pre>




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